- Email: [email protected]
Developing multimetric indices for monitoring ecological restoration progress in salt marshes
Marine Pollution Bulletin 64 (2012) 820–835
Contents lists available at SciVerse ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Developing multimetric indices for monitoring ecological restoration progress in salt marshes O.C. Langman a,⇑,1, J.A. Hale a,1, C.D. Cormack a,1, M.J. Risk b, S.P. Madon c,2 a
Pandion Technology Ltd., Suite #601, 6th Floor, 28th October Street, Limassol 4065, Cyprus PO Box 1195, Durham ON, Canada N0G 1R0 c CH2M HILL, Water Resources and Ecosystems Management, Ecosystem Planning and Restoration, 402 W. Broadway, Suite 1450, San Diego, CA 92101, USA b
a r t i c l e
i n f o
Keywords: Multimetric index Salt marsh Ecological restoration End states of restoration Restoration trajectory Adaptive management
a b s t r a c t Effective tools for monitoring the status of ecological restoration projects are critical for the management of restoration programs. Such tools must integrate disparate data comprised of multiple variables that describe restoration status, including the condition of environmental stressors, landscape connectivity, ecosystem resilience, and ecological structure and function, while communicating these concepts effectively to a wide range of stakeholders. In this paper we describe the process of constructing multimetric indices (MMIs) for monitoring restoration status for restoration projects currently underway on the eastern coast of Saudi Arabia. During this process, an initial suite of measurements is filtered for response and sensitivity to ecosystem stressors, eliminating measurements that provide little information and reducing future monitoring efforts. The retained measurements are rescaled into comparable domain metrics and assembled into MMIs. The MMIs are presented in terms of established restoration theories, including restoration trajectory and restoration endpoint targets. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Degradation of aquatic ecosystems, primarily from anthropogenic activities, has led to major efforts to regenerate, rehabilitate, or convert ecosystems towards a more desirable configuration (National Research Council, 1992). The motivation behind restoration projects is the restoration of ecological services and functions, which impact a wide range of stakeholders beyond restoration scientists that will need to be informed of the progress of the project. Despite the large body of theory that supports the development and design of restoration projects, it has been pointed out (Jones and Schmitz, 2009; Reeves et al., 1991;Roni et al., 2003) that monitoring efforts have often proven inadequate to quantify physical and biological responses within the ecosystems being modified. Given this possibility of failure and the importance of communicating ecological information to stakeholders, monitoring programs need to play several roles, including: (1) integrating the scientific knowledge and theories behind the design of the project into the monitoring program to include the measurement of appropriate stressor and response variables, (2) developing and implementing an analytical framework that evaluates monitoring data to provide the pertinent information needed to adaptively manage the ⇑ Corresponding author. Tel.: +1 608 3207761; fax: +1 803 2546445. 1 2
E-mail address: [email protected] (O.C. Langman). Tel.: +1 803 5135649; fax: +1 803 2546445. Tel.: +1 619 6870120x37233.
0025-326X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpolbul.2012.01.030
restoration to improve its chances of success, and (3) presenting the progress and condition of the restored ecosystem to the stake holders in a manner that is easily interpretable and understandable, yet based on valid scientific assessments. 1.1. Endpoints of restoration The endpoints of restoration have been described in terms of community structure as well as supporting chemical, biological, and physical processes (National Research Council, 1992). Descriptions matching this level of detail for the desired state of remediation sites are rare, which has lead to the practice of having reference ecosystems provide the basis for both developing remediation methodology and evaluating the progress of an ecosystem restoration (Society for Ecological Restoration, 2004). While a reference system can be used as a model for a desirable outcome of restoration, the restored site will at best approximate the condition of the reference site due to spatial variability, however, slight, in the physical, chemical, and biological gradients forming the basis of the ecosystem processes. Further variability within a reference system emerges from the innate non-static nature of an ecosystem, across season variability, community-level evolution, or natural progression of the reference systems to new states (Duarte, 1991; Horne and Schneider, 1995; Palmer and Poff, 1997). Sadly, the desire to force an ecosystem into an overly specified state is common, and has resulted in restoration ‘failures’ that are, for the most part, functional ecosystems in their own right (Simenstad and Thom, 1996).
821
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
More stress
Ecosystem stressors
Less stress
Remains the same
Degraded ecosystem Time
Target ecosystem
Re ha bi lita De tio gr Na n (p a t da rim ur a t io ar l p n y r su oce cc ss es e sio s n)
Alternate states
Ecosystem Function
Ecosystem Complexity / Function Stressors reduced via remediation activities
Degraded ecosystem
C
Target ecosystem
Rec (rep lamatio lace n men t)
B
Target ecosystems
Ecosystem health responds to reductions in stressors
Ecosystem health
A
Degraded ecosystem Ecosystem structure
Fig. 1. Theoretical spaces that describe the response of an ecosystem to remediation. (A) An original space that assesses the response of ecosystem health to the reduction of stressors (B) A mapping of ecosystem complexity through time, redrawn from Hobbs and Mooney (1993) (C) Evaluating ecosystem function (biomass, nutrient content, cycling) against structure (species diversity, complexity), redrawn from Dobson et al. (1997).
1.2. Trajectory Equally as important as the restoration endpoint is the progression from degraded to a restored ecosystem. Numerous conceptual models for the progression of a restoration site through time have been proposed (Dobson et al., 1997; Hobbs and Mooney, 1993; Hughes et al., 2005; Magnuson et al., 1980), with the term ‘‘trajectory’’ being used to describe the hypothetical pathway traversed during the restoration progress (Fig. 1).The multiple interpretations of restoration trajectory are based on which ecosystem attributes (e.g., ecosystem health, structure, and function) are being tracked (Fig. 1) indicating the difficulty of consolidating the requirements of ecological restoration even at a conceptual level. As a reflection of this, the theoretical spaces leave trajectories simplified, indicating a general direction and approximate endpoint. Practical applications of the trajectory concept have largely involved developing multiple trajectories for individual parameters, often indicator species, used to represent restoration status. The inherent variability of single parameters, for example, over stressor gradients and temporal/spatial scales, however, often results in inconclusive representation of restoration trajectory (Odum et al., 1995;Zedler and Callaway, 1999), and aggregating parameters into a single trajectory has proven difficult (Society for Ecological Restoration, 2004). An ideal trajectory would integrate disparate data that describe site condition (and thus restoration status), and provide information that may be used to adaptively manage the restoration project. 1.3. Adaptive management Without a regular assessment of restoration status supported by a well-developed monitoring program, a restoration site may follow a trajectory different from the desired outcome. While our understanding of succession is continually improving, knowledge of the current state of an ecosystem and the stresses that it will face during restoration will never be complete, leading to difficulty in making accurate predictions of site evolution over the duration of the restoration project. The proposed solution to this problem is to monitor the restoration status and to nudge the system toward the desired trajectory and adaptively manage it if a significant deviation is detected. According to Shreffler et al. (1995) , this concept is not new, but there are few examples in the literature that indicate the principle is being used. Possible reasons for this include insufficient funding for additional manipulation, lack of clear resolutions to the problems, or a lack of supporting data to drive
the management. In the latter case, the decision to re-engage in the manipulation of the restoration site can be improved by providing managers with a broader dataset that describes ecosystem status, and by extension, the range of problems that can occur during restoration. Actually describing the concepts of trajectory, restoration endpoints, and adaptive management within the context of a monitoring program remains a difficult task. Recent attempts at describing ecosystem status have moved in two distinct directions: (1) identifying organisms that can be used to integrate multiple signals from the ecosystem (indicator species); or (2) by collecting large amounts of data to produce community descriptors. Indicator species have been developed as the corner stone of monitoring programs (Metcalfe et al., 1984;Reynoldson, 1987). Monitoring programs based on indicator species are particularly attractive because acquiring the data is often time- and cost-effective. Several common assumptions about the relationship of indicator species with the greater community have proven unreliable, however, such as the idea that high species richness or habitat diversity is correlated with the occurrence of rare species (Pearson and Cassola, 1992), or that associations between species remain similar across a given habitat (Niemi et al., 1997). When evaluating ecosystem status, particularly in restoration projects where the successional trajectory can be short-cut through plantings, species introductions, and other modifications, the lack of reliability of these relationships reduces the value of using just species–community relationships for evaluating the condition of the ecosystem. Complex systems need to be described using a framework of many parameters, although this task can overwhelm the researcher with data. Important parameters for ecosystems include elements of structure, function, landscape connectivity, and resilience to perturbations, all of which must be addressed to evaluate the status of a restoration. Karr (1981) introduced a multimetric index (MMI) to represent elements of biological condition in a variety of different systems. Karr’s work stemmed from the use of water quality data as a surrogate for biotic assessment, in cases when biological condition could not adequately be characterized. Until then, water quality was primarily monitored chemically and physically by the EPA and other monitoring institutions, but despite extensive monitoring and management programs water quality continued to deteriorate (Davis and Simon, 1989; EPA, 1987; Karr, 1981). This resulted from not only having excess chemical data that were swamping managers, but from a lack of data on the biological processes working on the ecosystems (Karr, 1981). Since its introduction as a method of monitoring biotic condition
822
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
in streams, the use of MMIs has expanded to other ecosystems, but has proven particularly useful in aquatic ecosystems (Emery et al., 2003; Stoddard et al., 2008). The purpose of this paper is to use monitoring data to develop an MMI that integrates disparate datasets and produces results that can guide restoration efforts and provide useful information to managers. The MMI will be based on preliminary monitoring data from a salt marsh restoration project currently underway, located on the eastern coastline of Saudi Arabia, developed in response to persistent degradation that was a result of the largest oil spill in history. At the conclusion of the 1991 Gulf War, an estimated 6–11 million barrels of crude oil were intentionally released into the Arabian Gulf, affecting approximately 800 km of the Saudi Arabian coastline (Abuzinada and Krupp, 1994; Tawfiq and Olsen, 1993). Both primary and secondary (faunal burrows) porosity allowed oil to penetrate the substrate of salt marshes and nearly two decades later, this oil persists. The United Nations
Compensation Commission was formed to administer monetary claims against Iraq, and awarded $463 million USD toward the ecological restoration of degraded salt marsh along the Saudi Arabian coast, creating one of the largest ecological restoration projects in history. We were tasked with developing and implementing a monitoring plan and an analytical framework for the monitoring data to estimate the status of post-remediation recovery, a task which included managing the restoration projects as well as incorporating local and international stakeholders into the decisionmaking process. The procedure used to generate the MMI is as follows: Identify attributes of the restoration site that may be areas of concern. Create a list of potential measurements useful for evaluating these attributes Condense these measurements into a set of metrics.
Fig. 2. Location of reference marshes (C1, C2, C3, C4, C5), restoration sites (R1, R2, R3, R4, R5, R6, R7, R8, R9, R10), and the set-aside sites (S1, S2, S3, S4).
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
Test these metrics for utility, based on initial monitoring. Eliminate those metrics that show no difference between restoration and reference sites, or that have high measurement or temporal variability. Evaluate reference sites to establish appropriate endpoints for remediation. Examine the current state of the restoration project in order to provide managers with data that can be used to adjust the trajectory of the project. 2. Materials and methods 2.1. Study area All field work was (and continues to be) conducted within intertidal salt marsh systems on the eastern coast of Saudi Arabia, between Abu Ali and Batinah Islands in the south, and the Balbol embayment to the north (Fig. 2). This segment of shoreline represented the area with the largest amount of stranded oil from the 1991 Gulf War oil spill. In the period between January and May of 1991, an estimated 6–11 million barrels of Kuwaiti crude oil were pumped or released into the Arabian Gulf, creating the largest oil spill in history (Abuzinada and Krupp, 1994; Tawfiq and Olsen, 1993). Much of the oil was trapped in the south due to orientation of the islands and bays between Abu Ali Island and Tanajib. Within these bays and islands, large areas of sheltered mudflats and intertidal marsh systems were moderately to heavily oiled (Fig. 3) during the initial spill, with oil penetrating particularly deeply in marshes due to an abundance of burrowing organisms (Getter et al., 2005; Pandion Technology Ltd., and Research Planning Inc., 2003; Michel et al., 2005). A follow-up survey performed in 2002–2003 mapped the extent and toxicity of the remnant oil over
823
12 years after the initial oiling. Marsh areas that were described as moderately oiled in the 2002–2003 survey report contain oil that, when flooded, produces sheen and small droplets of liquid oil. Areas that were described as heavily oiled produced thick patches of liquid oil under similar conditions (Fig. 3). Salt marshes were determined to contain over 23% of the remnant oiled sediments, with total PAH (sum of 43 PAHs) of ranging from 3580 ng/g to 126,900 ng/g in areas with visible remnant oil, which included all of the restoration marshes in this study. Bejarano and Michel (2010) found that heavily oiled salt marshes had total PAHs at concentrations that were above sediment quality benchmarks and PAH distributions indicating slow weathering rates compared to other habitats. These marshes were fine-grained, low-energy systems; tidal range was 0.5 m, and inputs of fresh water were minimal. Productivity was assumed to be low due to limitations imposed by extremes of heat and salinity: during summer, air temperatures regularly exceeded 45 °C, and salinity in the channels reached 90ppt. Although no studies could be located that quantified primary production for the marsh intertidal zones in the Arabian Gulf, it is assumed that algae were responsible for a higher fraction of primary productivity relative to marsh systems in other parts of the world (Al-Zaidan et al., 2006). The dominant vascular plants were two succulent perennials, Arthrocnemum macrostachyum and Halocnemum strobilaceum, and two succulent annuals, Salicornia europea and Suaeda maritima. Benthic algal mats comprised of diatoms and cyanobacteria often covered much of the marsh surface, with diverse morphologies influenced by elevation, hydrology, and nutrient gradients (Kendall et al., 1968). The crustacean community’s main constituent was Nasima dotilliforme, with Macrophthalmus sp. occupying some channel bottoms and low elevations extending into seaward mudflats, and the predatory
Fig. 3. Examples of various levels of oiling. (A) Heavy oiling located on the marsh surface due to oil trapped in non-burrow pore spaces. (B) Moderate oiling in channel bank due to oil trapped in relic burrows by laminate mat covering. (C) Moderate to light oil sheen in channel bank. (D) Moderate oiling from relic small (insect/polychaete) burrows in channel bank.
824
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
Fig. 4. Photograph of an excavation that shows a cross-section of laminated algal mat. Individual layers of sedimentation and algal growth are readily visible. Laminate mats grow as thick as 10 cm in some areas. An oil-lined burrow that was located below the laminate mat has been placed in the upper left of the photograph.
Metopograpsis messor, Metaplax indica, and Eurycarcinus orientalis appearing sporadically. Other invertebrates included the snails Pirinella conica and Nodilittorina sp., as well as numerous burrowing polychaetes and amphipods. Secondary effects from the oil spill have further impaired natural recovery of the salt marsh. In many areas of degraded salt marsh, the lack of burrowing fauna and algal grazers has allowed the algal mat to develop into a thick, laminated covering over the marsh surface (Fig. 4), impairing recovery by acting as a barrier to re-colonization, and preventing further weathering of the remaining oil (Al-Thukair and Al-Hinai, 1993; Barth, 2003, Pandion Technology Ltd., and Research Planning Inc., 2003). The thickness of the laminated mats ranged between 5 and 25 cm, with the thickest mat located in depressions on the surface of the marsh and degraded tidal channels. In these cases, the secondary effects were complicated further by the algal mat impairing marsh hydrology, which created a positive feedback with the algal mat, which thrived in areas with limited drainage. The remediation and restoration activities planned for the restoration sites include excavation of existing or impaired tidal channels (blocked primarily by algal mat), creation of new tidal channels, sediment tilling and direct treatment of algal mat, and transplantation of local flora, primarily Avicennia marina. While remnant oil still exists within these marshes, the focus of the restoration efforts will be on accelerating the natural recovery of these systems rather than the removal of oil. Most of the remnant oil in the marshes is contained within collapsed burrows of Nasima and other burrowing organisms, with oil deposits occurring at depths of up to 0.6 m (Pandion Technology Ltd., and Research Planning Inc., 2003; Michel et al., 2005). No feasible method for locating and extracting the oil from these burrows exists, and attempting to expose burrows through tilling or other invasive methods would impair any natural restoration that has occurred and potentially expose adjacent areas to re-oiling. Since some of the areas have recovered naturally to a large degree, the restoration activities will only be employed in areas where they can reasonably be expected to improve the sites. Many of the marshes targeted for restoration within this study appear to have stabilized in various states of natural recovery, but all are expected to benefit further from targeted restoration activities. 2.2. Site selection and sampling strategy Following an initial coastline survey in late 2009, marshes were prioritized for remediation activities based on a rapid assessment protocol that focused on evaluation of overall ecosystem health
(Hale et al., 2011). Salt marshes were placed along a gradient of disturbance that ranged from no impact to heavy impact, based primarily on an ecological evaluation. Sites identified as having received little to no impact during the initial oiling event, or sites that have since been described as sufficiently recovered, were used as reference marshes. Sites that were initially heavily oiled and have since had little natural recovery were divided into sites targeted for remediation, and set-aside sites, which will not be restored and serve as a baseline for monitoring natural recovery. This study incorporated data from five reference sites. Four of the reference sites were not impacted by the initial spill, the fifth was deemed to have recovered sufficiently due to statistical similarity with the non-impacted sites, and has been classified as recovered by other authors (Höpner and Al-Shaikh, 2008). The initial set of five restoration sites was selected from a range of impact levels and levels of ecological recovery between a medium level of impact to a high level of impact. Reference sites were located close to impacted sites when possible, although the number of reference sites was limited due to the high extent of oiling that occurred along the coastline. Field sampling was divided into biannual efforts, with the first year’s monitoring efforts represented here. Fall sampling occurred between September and November of 2010, and spring sampling occurred between February and April of 2011. Spring sampling occurred over 19 sites, including 10 impacted sites, 5 reference sites, and 4 set-aside sites. The sampling windows helped to reduce the effect of temporal variability of the field measurements, which is occasionally a concern for MMIs (Blocksom, 2003). The field program was designed to detect changes in environmental attributes that differ between reference and restoration sites due to primary or secondary effects from the oiling, and to detect changes that were the direct result of, or affected by, the remediation activities performed during the salt marsh restoration. Salt marsh restoration sites ranged from around 5 ha to larger than 80 ha in size, making sub-sampling desirable for most aspects of monitoring, and particularly for determining species diversity and density. Within each site, natural recovery was most evident along channels and in the lower elevations of the marsh, possibly reflecting differing rates of recovery relative to the degree of tidal flushing and initial vertical oil penetration. To ascertain the relative importance of elevation and channel proximity to the rate of recovery, nine sub-sampling plots were located within each reference and restoration site. The nine plots were placed at three relative elevations on the marsh surface, at low, medium, and high elevations, which, based on the reference marshes, corresponded to three distinct communities that differ primarily in the abundance and spatial distribution of the organisms present. For example, Salicornia europea is found abundantly on the marsh surface in the low elevation, only along channels in the medium elevation, and is largely absent in the high elevation plots. Despite the marked differences among the communities, the actual elevation range for the marsh surface rarely exceeded 30 cm from the mudflats that marked the end of the low elevation to the toe of a narrow sand beach that typically marked the extent of the upper marsh. At each elevation, two plots were associated with channels, and one plot was placed in the interfluvial space on the marsh surface between channels. The channel-associated plots were placed adjacent to two different channels within the same channel network of different order. Depending on the configuration of the marsh, these channels may either to be first and second order, or second and third order, but the larger of the two channels was between 1 m and 3 m wide and the smaller was <1 m wide. Data were collected and archived independently for each plot within a site for each season, and later combined during analysis. It was hypothesized that elevation and proximity to the channel
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
825
Table 1 List of attributes of restored ecosystems (SER, 2004). Attribute
Description
1
The restored ecosystem contains a characteristic assemblage of the species that occur in the reference ecosystem and that provide appropriate community structure The restored ecosystem consists of indigenous species to the greatest practicable extent. All functional groups necessary for the continued development and/or stability of the restored ecosystem are represented or, if they are not, the missing groups have the potential to colonize by natural means. The physical environment of the restored ecosystem is capable of sustaining reproducing populations of the species necessary for its continued stability or development along the desired trajectory. The restored ecosystem apparently functions normally for its ecological stage of development, and signs of dysfunction are absent. The restored ecosystem is suitably integrated into a larger ecological matrix or landscape, with which it interacts through abiotic and biotic flows and exchanges. Potential threats to the health and integrity of the restored ecosystem from the surrounding landscape have been eliminated or reduced as much as possible. The restored ecosystem is sufficiently resilient to endure the normal periodic stress events in the local environment that serve to maintain the integrity of the ecosystem. The restored ecosystem is self-sustaining to the same degree as its reference ecosystem, and has the potential to persist indefinitely under existing environmental conditions.
2 3 4 5 6 7 8 9
networks may influence the rate of recovery, which required that the data be separable based on these criteria. Furthermore, seasonal data were kept separate to allow for analysis of marsh progression through time. The data collected at each plot included measurements that assessed elements of ecosystem structure, function, and remnant anthropogenic effects within each site. Using the SER’s nine attributes of restored ecosystems as a guide (Table 1), a wide range of measurements were taken to assess current ecosystem status (Society for Ecological Restoration, 2004). For each of the SER attributes that applied to this project, several measurements that will later provide the information for developing ecosystem metrics, and that would represent each attribute in the overall restoration status, were identified (Table 2). The initial field samplings included as many of these measurements as could feasibly be measured by the field crews, with the assumption that some of these measurements would prove to be non-informative for the overall monitoring program and would later be dropped from the field program. 2.3. Metric filtering Once the initial monitoring was complete, the measurements were further developed into metrics. A ‘metric’ was defined as a value that could be derived from one or more discrete or continuous measurements of the marsh or surrounding landscape. Metrics largely coincided with the planned measurements in Table 2, but often the individual measurements could be utilized by one or more metrics, which were further broken down into components where possible. A metric that was derived from three measurements but could potentially be represented by a combination of two measurements or any individual measurement was developed into every possible combination. For example, the health of benthic infauna could be described as simple presence/absence of species, density measurements of the species, or by measurements of a subset of species present. A metric was developed for each possibility resulting in several, often highly correlated, metrics derived from the same source data. This process, combined with filtering at a later stage, resembles a rarefaction analysis. Categorical measurements required conversion before they can be interpreted as metrics. An example of this was the algal form observations, which were converted into a rank value arranged from least to most desirable. Algal forms that were regularly observed in reference marshes are assigned a value of 1, while polygonal laminate and flat laminate algal forms were assigned values of 3 and 5, respectively, representing their relative impact on hydrology and impairment to colonization (Fig. 5).
2.3.1. Range Metrics that have limited ranges were not useful for describing differences between marsh systems. Discrete variables such as rare organism abundances may lack the statistical power needed to provide useful information. Continuous variables occasionally exhibit measurement error that exceeds the range of the measurement. To address these concerns, discrete metrics were eliminated if their range was 2 or less. Continuous measurements were eliminated if their range was within the estimate of error for the measurement. For example, visual estimates of percent cover were determined to be ±5% based on photographic post-analysis of field estimates, so if the range fell under the 10% threshold, the metric was discarded. The criteria for discarding percent cover metrics matches Klemm et al. (2003), although it was unclear how Klemm et al. arrived at their rejection threshold. Other continuous metrics had rejection thresholds ranging from 7% to 35%, and were created using estimates of measurement variability derived from repeated samplings using different people and equipment. Finally, any metric where >90% of the values were 0 was discarded, such as measurements of abundance of snails of the genus Cerithium, which were rarely found within either the reference or restoration sites. Metrics that failed the range test were eliminated from further consideration. 2.3.2. Response to disturbance Within the context of a restoration due to anthropogenic damage, a metric must have been be able to distinguish between reference and impacted sites. Each site was classified according to the initial survey as ‘‘impacted’’, ‘‘set-aside’’, or ‘‘reference.’’ Prior to remediation activities, the ‘‘set-aside’’ and ‘‘impacted’’ classifications were treated as a single classification. A one-way analysis of variance (ANOVA) was used to test each metric for its ability to classify set-aside/impacted and reference sites. Significant metrics (F-value significance of p 6 0.05) were preserved, while metrics that was incapable of distinguishing between set-aside/impacted and reference sites were discarded. 2.3.3. Signal-to-noise ratio Metrics that exhibited a large amount of within-site variability relative to the variability between sites did not contribute adequate information to the index. The signal-to-noise ratio was calculated as the variance over all of the sites (impacted, set-aside, and reference) as the signal, divided by the variance of the metric between seasonal visits at individual sites as the noise (Kaufmann et al., 1999). Various ratio thresholds for retaining metrics have been suggested during the development of other MMIs, ranging from >1.5 to >3 (Klemm et al., 2003; McCormick et al., 2001,
826
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
Table 2 Target measurements and SER attribute associations. SER attribute
*
Measurement
References
Characteristic assemblage of species, intact community structure Amphipod abundance Active ocypode burrow counts Benthic infauna abundance Species richness
Gómez Gesteira and Dauvin (2000) Adam (1990) Sacco et al. (1994) Jones and Richmond (1992), Jones et al. (1998)
Presence of invasive species* Invasive richness Invasive diversity
Mooney and Hobbs (2000) Mooney and Hobbs (2000)
Presence and condition of key functional groups Perennial halophyte abundance Perennial halophyte canopy cover Gastropod abundance Ocypode abundance
Adam (1990) Adam (1990) Peck et al. (1994) Adam (1990)
Physical environment which supports biota Channel, porewater, and ponded salinity Substrate temperature in rhizosphere Burrow abundance on channel banks Burrow abundance in perpendicular transect extending onto marsh surface
Broome et al. (1988), Portnoy (1999), Howard and Mendelssohn (1999) Callaway and King (1996), Lindig-Cisneros and Zedler (2002) Adam (1990) (Original)
Normal ecosystem function and development Marsh surface drainage/hydrology Subsurface hydrology Perennial growth rates
Montalto and Steenhuis (2004) Montalto and Steenhuis (2004), Osgood and Zieman (1998) Howard and Mendelssohn (1999)
Integration into landscape Fish abundance Fish size distributions Fish gut contents C,N,S stable isotope analysis Bird foraging Bird diversity
Chamberlain and Barnhart (1993) Madon et al. (2001), Weisburg and Lotrich (1982) Allen et al. (1994) Kwak and Zedler (1997), Peterson et al. (1986) Brawley et al. (1998) Warren et al. (2002)
No threats to adjacent systems Analyses of foraminifera
Morvan et al. (2004), Sabean et al. (2009)
Resistance and resilience Shannon–Weaver diversity Euclidean distance index for functional attribute diversity Molecular biomarkers
Tilman (1996), Yachi and Loreau (1999), Naeem and Li (1997) Walker et al. (1999) Downs et al. (2001a,b)
Self-organizing, self-sustaining ecosystem Channel morphology Annual halophyte abundance
D’Alpaos et al. (2005), Phillips (1999) Adam (1990)
No invasive species were detected in the study area.
respectively), but little support for the different thresholds was apparent. After examining the response of the metrics that demonstrated significant response to the stressor gradient with signal to noise ratios between 0 and 5, we determined that a threshold of 3 was a tolerable compromise between eliminating variables with limited information and ensuring that the SER attributes were adequately defined by the measurements collected. Fig. 6 demonstrates the difference between a metric with a high signal-to-noise ratio (Nasima density) and a low signal-to-noise ratio (percentage of exposed sediment within a quadrat), with the latter serving as an example of a rejection based on the signal-to-noise criteria. Further consideration is given to address issues of metric scoring below. 2.4. Metric scoring The diversity of values and types of measurements that comprised the metrics required that they be rescaled to comparable values before they were integrated into a single multimetric value. Specifically, sites were scored continuously to the range 0–100, with the lower threshold of 0 representing the worst or most undesirable condition and the upper threshold of 100 representing the most desired condition. Several metrics required transformations before they could be rescaled to address directionality and
distribution concerns (Table 3). Metrics that had higher values representing worse condition needed to be inverted to match the desired scheme. For example, algal mat cover, a percentage metric, was considered more degraded at higher degrees of cover, which was addressed by inverting the cover values for algal mat cover. Metrics that were recorded as percentages originally derived from count data needed to be arcsine transformed to convert from a binomial distribution to a normal distribution. All such transformations were applied prior to the rescaling. The low number of sites made performing a power analysis (as per Blocksom, 2003) impossible, but the guidance provided by Blocksom’s analyses aided in the choice of rescaling algorithms, resulting in the selection of a continuous scaling method. Several MMIs in the literature have used the 95th percentile value of a metric to determine the upper bound and the 5th percentile to determine the lower bound used in the transformation for a specific metric (Blocksom, 2003; Stoddard et al., 2008).The limited number of sites available in this restoration project, however, allowed individual sites to alter these bound values drastically, meaning that the use of percentile-derived boundaries for rescaling may provide limited benefit. Instead, the minimum and maximum observed values were used as the upper and lower bounds. In Eq. (1), S is the rescaled metric. The mean of the set of measurements M is linearly rescaled between 0 and 1 and then multiplied by 100.
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
827
Fig. 5. Photographs of three algal forms. (A) A typical ‘‘laminate’’ algal form, with an average thickness 52 mm. (B) A typical ‘‘folded’’ algal form, with an average thickness of 1 mm. Note that this form is regularly seen in the reference marshes. (C) A typical ‘‘polygonal laminate’’ algal form, with an average thickness of 48 mm.
100
Metric Value
75
50
25
0 Nasima density (Reference)
Nasima density (Impacted Sites)
% Exposed Sediment (Reference)
% Exposed Sediment (Impacted Sites)
Fig. 6. Demonstration of signal-to-noise ratios. Both the Nasima density metric and the percentage of exposed sediment (i.e., not covered by algal mat or asphalt pavement) metric have significant values for responsiveness to the disturbance, but the percentage of exposed sediment metric fails the signal-to-noise ratio test. Signal-to-noise values for Nasima density and percent exposed sediment are 3.14 and 1.21, respectively.
S¼
M lower 100 upper lower
ð1Þ
The inability to correct for potential outliers without drastically increasing the number of set-aside and reference sites indicated that reducing measurement and seasonal variability was
particularly important. Increasing the signal-to-noise threshold for metric inclusion reduced the potential for a variable metric to cause rescaling to affect interpretative capacity. For this reason, the signal-to-noise threshold of 3 was preferred over lower literature values to stabilize the edge effects from variability.
828
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835 Table 3 List of metrics and filtering statistics. Metric
Range (pass/fail)
Responsiveness (p-value)
Signal-to-noise
Transform
Algal form rank Algal mat% cover Amphipod density Arthrocnemum% cover Arthrocnemum count Asphalt pavement% cover Avicennia% cover Avicennia count Cerithidea density Channel belt transect – Nasima burrows Channel belt transect – total burrows Exposed sediment% cover Halocnemum% cover Halocnemum count Macrophthalmus density Metopograpsis density Micro-channel% cover Nasima density Nodilittorina density Ocypode predator:prey ratio Ocypode richness Pirinella density Ponded water% cover Pneumatophore density Salicornia density Salinity – channel Salinity – ponded Water Salinity – porewater Scopimera density Sediment temp in rhizosphere Shannon index – all organisms Small insect burrow density Snail richness Suaeda density Total perennial% cover Total perennials count Total snail density Total species richness Visual oiling estimate Whole plot ponding
Pass Pass Pass Pass Pass Fail Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass
0.02 0.00 0.04 0.06 0.14 0.00 0.68 0.03 Undef 0.00 0.00 0.00 0.03 0.98 0.19 0.32 0.03 0.01 0.00 0.34 0.00 0.25 0.08 0.68 0.65 0.11 0.74 0.35 0.48 0.00 0.04 0.59 0.16 0.78 0.04 0.08 0.25 0.03 0.83 0.05
1.19 44.82 5.26 1.09 1.10 1.80 Undef 2.99 Undef 22.35 27.03 1.46 4.61 126.28 1.60 20.47 12.07 3.78 1.80 25.32 4.61 12.78 3.65 1647.59 3.66 4.30 7.73 1.19 0.89 37.51 3.17 1.29 2.59 3.29 126.78 1.41 13.10 4.35 3.73 3.59
None Invert None Arcsine None Invert Arcsin None None None None None Arcsin None None None None None None None None None Invert None None None None None None None None None None None Arcsine None None None None Invert
2.5. Categorization of datasets/treatment of redundancy Once the metrics were filtered and rescaled, the remaining survivors were grouped with the SER attribute that they most closely represent. After the metrics were assigned, there were several possibilities: (1) an SER attribute lacked any descriptive metrics; (2) an attribute was assigned exactly one metric; or (3) an attribute was represented by several metrics. In the first case, the metric either could not be evaluated for the site and needed to have additional metrics considered to evaluate the attribute or, more desirably, the attribute was not a concern for the remediation project. In the second case, the metric would directly represent that SER attribute. In the third case however, the metrics needed to be examined for redundancy and combined into a single metric. Redundancy was evaluated using Pearson’s product-moment correlation coefficient, with a rejection of a metric correlated with another at r P 0.75. If two metrics were redundant, the metric with a lower signal to noise ratio was discarded. The remaining metrics were added into a single index. The resulting combined values were rescaled. 3. Results This method of use and reuse of measurements produced an excess of metrics. This initial suite of measurements contained more information than strictly needed to evaluate marsh condition, with the assumption that many of the metrics would prove redundant or uninformative for evaluating restoration status. As a
consequence, the reduction of the set of metrics, and by extension the measurements taken during the field program, was a process integrated into the MMI development. Measurements that were rejected due to statistical insignificance or redundancy could be dropped from the sampling program altogether, which would allow researchers to focus valuable field time (and efforts) on relevant metrics. To aid this process, a single metric could be split into a gradient of complexity; if a metric was derived from two components and could potentially be represented by either one of those components, the individual components were included in case they were statistically sufficient to describe the desired attribute. For species presence and density metrics, often the metric was not significantly improved by incorporating the density of rare species, and so the effort needed to quantify the rare species can potentially be reduced. For example, the density of the predatory Metopograpsis was less informative (lower signal-to-noise ratio) due to high variability in densities than simply noting the presence or absence of the organism at a site. Metrics that had sufficient range, were repeatable (high signalto-noise ratio), and exhibited a response to the disturbance were integrated into the field program and monitored for two seasons, spring and fall. Whole ecosystem development and recovery were expected to be slow, with perennial vegetation and soil organic content potentially taking decades to recover to reference status. The most marked differences in salt marsh status between seasons will be visible during the spring recruitment period. Preliminary studies have shown rapid re-colonization of excavated channels by burrowing infauna, including amphipods and Nasima.
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
3.1. Metric selection Metrics were selected for possible inclusion into the MMIs out of a total of 72 possible metrics. Of the 72 metrics, nearly half (32) were alternative forms of metrics and were discarded as underperforming versions produced as part of the rarefaction analysis used to generate more efficient metrics. While this rarefaction approach produced an excess of metrics, the assumption was that most of these metrics were redundant, but occasionally a metric only required a subset of the sampling effort to obtain adequate information. In practice, only the best performing metric in terms of range, responsiveness to disturbance, and signal-to-noise ratio was preserved. Out of the remaining 40 metrics, 26 metrics were rejected (Table 3).Three of the metrics failed the basic range test, 19 failed the response test, and 4 failed the signal-to-noise ratio test. The visual oiling estimate failed to distinguish between reference and remediation sites. This was presumably a response to the methodology used to initially classify the sites; the initial classification considered the ecological condition, regardless of current visual oiling, allowing a recovered but oiled site to persist as a reference site. In addition, areas that contained relatively little remnant oil occasionally exhibited a large degree of secondary impact in the form of extensive algal mats. As a result, the visual presence of oil proved not to be a good metric. Instead, secondary effects that presumably developed in response to the oiling, including pervasive, laminated forms of algal mat and degraded hydrology, were used to represent the stressors acting on the marsh. 3.2. SER attribute axes Three of the nine SER attribute MMIs are presented here, each exhibiting a distinct difference in condition between the impacted and reference sites (Figs. 7–9). Attribute 4 (Fig. 9), which represents aspects of the physical environment that support local biota,
829
is of particular note. In this case, the impacted sites had a large range of variability. The differences were likely due to the densities of the tidal channel networks; algal mat has in-filled many of the channels at the sites that performed the most poorly on this axis (R5, R7, R8, R9), while some of the sites that performed markedly better had more intact networks (R2, R3). This difference reflects the remediation and restoration plans for these sites; the sites with better condition will receive less focus on physical environment. The other attributes showed similarly degraded conditions across the impacted sites, particularly across the attributes that represented floral and faunal structure (Figs. 7 and 8). From the pre-restoration plot of an individual attribute, it was possible to tell whether the attribute was degraded in the marsh system. An ANOVA with a significant f-value (see Figs. 7–9) indicated that the attribute in question was degraded relative to the reference marshes. If there was no detectible difference, the attribute was not considered degraded within the restoration sites. Comparison of magnitude across attribute MMIs was meaningless due to metric rescaling. An integrated MMI (Fig. 10) was created by taking the entire collection of metrics and incorporating all of them into a single MMI using the same process used for individual attribute MMIs. Some metrics included in individual attribute MMIs were excluded due to high correlation with metrics from other attributes, resulting in a smaller total subset of all of the considered metrics. Like the attribute MMIs, individual metrics were equally weighted. 3.3. Stressors axis The stressors MMI combined metrics that represented elements of marsh condition that were directly targeted by the restoration and remediation activities. In this case, the activities included expansion and repair of drainage networks, removal and breakup of the algal mats, and perennial transplants, which were designed to improve drainage, reduce laminate algal mat cover, and
Fig. 7. MMI describing the characteristic assemblage of species and community structure (SER attribute #1) plotted against a separate index describing the environmental stressors. The y-axis is comprised of the following metrics: species richness, amphipod presence and abundance, Nasima presence and abundance. The grey oval indicates the restoration target for this attribute. Significant difference between impacted and reference sites (ANOVA, p = 0.0010).
830
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
Fig. 8. MMI describing the presence and condition of the key functional groups of organisms (SER attribute #3) plotted against a separate index describing the environmental stressors. The y-axis is comprised of the following metrics: Shannon–Weaver (Shannon–Weiner) diversity index, perennial cover, and the presence and abundance of predatory ocypodes. The grey oval indicates the restoration target for this attribute. Significant difference between impacted and reference sites (ANOVA, p = 0.0002).
Fig. 9. MMI describing the condition of the physical environment (SER attribute #4) plotted against a separate index describing the environmental stressors. The y-axis is comprised of the following metrics: porewater salinity, relative sediment temperatures, and the abundance of evidence of burrowing crabs on channel banks. Significant difference between impacted and reference sites (ANOVA, p = 0.0076).
potentially increase the rate of degradation of the remaining oil. These activities were selected to offer the greatest chance of recovery for the effort expended, and do not necessarily represent attempts to address all of the stressors observed at each site. For
example, the removal of remnant oil (itself a stressor, particularly for burrowing fauna) from sealed infaunal burrows was determined to be infeasible due to the difficulty of locating burrows with pockets of remnant oil as well as actually extracting the oil. All of the re-
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
831
Fig. 10. MMI integrating all surviving metrics across all SER attributes. The y-axis is comprised of the following metrics: Nasima abundance, Amphipod abundance, perennial cover, integrated snail abundance, porewater salinity, relative sediment temperatures, and the abundance of evidence of burrowing crabs on channel banks. Significant difference between impacted and reference sites (ANOVA, p = 0.0001).
100 90 80 70 60 50 40 30 20 10 0
Fig. 11. Time series plot of the Ecosystem health MMI (Fig. 10) for three reference marshes. Marsh C1 exhibits marked seasonal differences that do not appear in marshes C2 or C3.
sponse axes (plotted on the vertical axis) were plotted against the same suite of stressors (plotted on the horizontal axis), since the primary concern of the monitoring program is to evaluate the effectiveness of the restoration and remediation techniques (Figs. 7–10). 3.4. Marsh restoration target A marsh restoration target (Figs. 7–10) was developed by calculating the standard deviation on the reference marshes
for each axis and creating an oval centered on the reference marshes to serve as a target. While this was contrived, it was useful because it integrated all of the elements of the SER attributes that described a successfully restored ecosystem, provided the attributes were represented and were given equal weight. Similar restoration targets may be developed for individual attributes, allowing for characterization of partial restoration successes. The restoration target for all of the marshes was defined as the space occupied by the reference marshes with a
832
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
buffer of one standard deviation of the variability of the target marshes. 3.5. Natural gradients No adjustments were made to address association between the metrics that may be correlated with the natural conditions that determined the initial degree of oiling. The topography of individual marshes varied greatly; several marshes contained dense dendritic channel networks and shallow pools while others lacked pools or displayed a lower density of tidal channels. The marshes were generally located across several different sheltered bays with surprisingly distinct tidal regimes due to two competing amphidromic centers, and sediments that were primarily muddy but with a varying sand component. Despite this surficial topography, sediment variability, and a 100 km distance between the northernmost and southernmost marshes involved in this study, the floral and faunal communities were similar across the reference marshes. It appears that the most important factor in determining the degree of oiling at a site was the predominant (NW) wind direction and magnitude during the initial oiling event (Pandion Technology Ltd., and Research Planning Inc., 2003).Thus, direction of the marsh exposure was correlated with many of the metrics which resulted in reference sites within the impacted area generally having a southern exposure. 3.6. Seasonal changes Due to seasonal rescaling and re-evaluation of metrics, it was hypothesized that seasonal differences would not significantly affect the output of the MMI generation process. One of the reference sites, however, suggests that this expectation was unfounded (Fig. 11). Site C1 exhibited a significant amount of seasonal variation derived from a robust infaunal community (primarily amphipods, Grandiderella sp.) that was delayed with respect to the other reference sites in the spring but which had established itself by the fall season sampling period. As a result, the MMIs that consider the infaunal community for the spring sampling periods rate site C1 poorly. Since other reference sites lacked that particular dynamic or other unique local seasonal changes, the seasonal variation was not as pronounced for other sites.
ecosystem, problems can be localized, and an adaptive adjustment can be implemented. 4.1. Marsh restoration target Restoration targets have been defined in many ways, such as providing habitat for a target species, meeting some minimum of functional performance, or meeting a structural requirement (Ehrenfeld, 2000;Zedler, 2001). In each case, reference systems that meet the requirements define the target ecosystem and likely span a gradient of potential conditions that will support the target goals. For this restoration project, the reference marshes were selected to include a range of healthy marsh configurations, partially due to the limited selection of reference sites due to the high degree of oiling along the coastline, but also to avoid over-specifying the restoration endpoint. Variability with the resulting ecosystems is tolerable as long as it resembles other systems in the area (Sacco et al., 1994). The configuration of the reference marshes varies in some measurements, but is remarkably similar in others. For example, the Arthrocnemum density metric had a range of 81 (after rescaling) for the reference marshes alone, indicating an immense degree of variability within the reference marshes for that metric. Channel density as estimated by analysis of satellite imagery and porewater salinity, however, were quite similar across all of the reference marshes. Porewater salinity in healthy sites ranged from 60 to 95 ppt, with a mean value of 82. Salinities at impacted sites were even higher, regularly exceeding the detection limits of the equipment (100 ppt), likely due to poor drainage (flushing) and the retention of water in the substrate. Variability of the traits is reflected in the size of the individual attribute targets, with larger acceptable ranges appearing for highly variable marsh characteristics. Despite these differences, the integrated, whole ecosystem MMI scores were similar (Fig. 10), indicating that there were likely several configurations that had similar overall complexity and organism densities represented within the reference marshes. Smaller restoration projects often do not have the benefit of a monitoring program large enough to characterize variability across multiple reference marshes. This is one possible reason why some projects fail to meet the specific criteria set out as restoration targets, despite the marsh forming into a viable and desirable ecosystem (Simenstad and Thom, 1996). 4.2. Trajectory
4. Discussion The primary concern of a restoration project is to initiate or accelerate the recovery of all attributes of a degraded ecosystem toward a more desirable state, with respect to ecosystem health, function, sustainability, and ongoing impacts to adjacent landscape (Society for Ecological Restoration, 2004). In practice, this has proven exceedingly difficult, which can be inferred based on the number and frequency of failed restoration projects (Hobbs and Norton, 1996; National Research Council, 1992). Failures happen for any number of reasons, including unexpected spatial and temporal variability in the physical setting, misunderstood relationships and interactions between resident organisms, and changes in the human desires driving the restoration. The process presented herein is derived from the example of establishing a monitoring framework for the restoration program on the eastern coastline of the Kingdom of Saudi Arabia. This methodology will be transferable to different habitats and different regions and will identify when the restoration as a whole is underperforming, and will present the relevant information in terms of the prevailing theories behind ecosystem restoration, including trajectory and restoration targets. By further compartmentalizing the attributes of a restored
The restoration trajectory concept is particularly intuitive, which makes it useful for communicating restoration status, of particular use when incorporating stakeholders into the decision making process. In most of the two-dimensional restoration spaces described in the literature (Fig. 1), restoration trajectories were represented as a smooth line leading directly from the degraded state in the lower left corner to the desired system state in the upper right corner. The MMI attribute and stressors spaces were intentionally set up to mirror this configuration. The trajectory concept, however, was likely oversimplified. In most of these spaces, ecological restoration was portrayed as degradation in reverse (Bradshaw, 1987). The recovery may involve feedbacks that are not reversible, however, such as salinization or nutrient shifts that impair recovery (Prober et al., 2002; Davis et al., 2003). Alternatively, the restoration activities may not promote response across all aspects of an ecosystem, and may negatively impact the site during restoration activities. While these incongruities and sources of variability will make the trajectory less clean, the overall movement from a degraded to a restored condition is preserved. Non-technical stakeholders will benefit greatly from the intuitive representation of restoration progress, potentially encouraging ownership in the project. If the ‘‘ecosystem health’’ descriptor is too generic (Fig. 10) for the
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
intended audience, the individual SER attribute MMIs can be presented using the same trajectory and restoration target concepts (for example, Figs. 7–9), further expanding depth of understanding while preserving the method and simplicity of interpretation. Trajectory in the attribute-stressor spaces presented in Figs. 7– 10 will not follow a strictly linear pathway from lower-left to upper-right due to non-linear response. The stressor axis represents the measureable elements that will be directly affected by activities undertaken to accelerate restoration at the site, including primarily metrics of hydrology and algal mat cover. After restoration activities are performed, which include direct manipulation of algal mat cover as well as increases in channel density and improved hydrology, the stressor index is expected to improve immediately, although not to the levels of the reference systems. Further desiccation and natural reduction in algal mat is expected as a result of increased drainage, which will only occur over an as yet unknown period of time, presumably at least several months, following the restoration of hydrology and the re-introduction of grazers. The relative rates of recovery present many possibilities for recovery trajectory, all of which are desirable. A problem occurs if the attribute stabilizes outside of the acceptable restoration targets, or if it begins to decline toward a degraded state. For example, Harris et al. (1985) described a phenomenon in which carbonate crusts formed in the intertidal zone as a result of repeated wet/ dry cycles. Such crusts have been observed on tidal channel banks within the study region and appear to limit the habitat available for burrowing organisms and also provided hard substrate for the establishment of sessile species (particularly Euraphia sp.) that are foreign to a soft sediment environment. The development of cemented crusts post-restoration would drive the trajectory towards an undesirable endpoint. The rapidity of detecting changes in status of a restoration site depends on the rate of natural regeneration. In our case, evidence from preliminary trials suggested that infauna rapidly recolonize, particularly along channels, but there was little evidence for a rapid recovery of the perennial vegetation, possibly due to degraded hydrology or the establishment of laminated forms of algal mat. Similarly, attributes based largely on infaunal evidence are expected to change more quickly than attributes based on perennial vegetation. 4.3. Natural recovery As presented, these MMIs represent the components of ecosystem recovery that can be attributed to anthropogenic contributions. The set-aside sites are particularly important, since they will continue to move along their own, natural trajectory (not necessarily toward the restoration target) independent of the sites affected by remediation and restoration activities. The seasonal rescaling that is performed during the creating of the MMIs creates a moving bar against which progress is measured, however, as long as the set-asides and restoration sites were similar with respect to their conditions prior to the restoration activities, this simply results in the natural recovery (or decline) effectively being factored out. This makes evaluating the anthropogenic contribution to the marsh restoration apparent and assists in answering whether the restoration activities have aided site recovery, since movement within the MMI space should be a response to restoration activities. One potential issue with rescaling, however, is that the seasonal rescaling is particularly sensitive to measurement variability within the set-aside and reference sites. Measurement variability, particularly for the set-aside sites and reference sites, can result in shifting the MMI values several points in either direction, depending on the magnitude of the measurement variability. The mean value for the observed shift for the set-aside and reference sites in the Ecosystem Health MMI to date is 2.2, a measurement which can be used to track how much measurement
833
distortion is present in individual seasons. Increasing the number of reference sites and set-asides and reducing the measurement variability through any available means are both recommended to help deal with this issue. 4.4. SER attribute MMIs The development of individual MMIs for each restoration attribute is designed to provide additional information in the event that a restoration project is underperforming. Indeed, partial successes are the majority result for ecosystem restoration projects (Lockwood and Pimm, 1999). While it is not feasible to monitor every metric that could indicate underperformance in a restoration project, the SER attribute MMIs attempt to ensure that the subset of metrics that are monitored over the longterm at least track important and measurable differences between the impacted and reference systems. The stakeholders in a restoration project, usually composed of people of disparate backgrounds and education, ultimately determine when a restoration project will take place, the acceptable endpoints for the remediation, and when or if additional measures need to be taken to guide the natural development of a restoration site. The MMI framework developed herein presents restoration status in a way that closely matches elements of restoration theory that are particularly intuitive, including restoration targets and trajectories. The breakdown of the whole-ecosystem MMI into components along the lines of the SER attributes of a restored ecosystem further extends the interpretive capability by preserving the assumptions made from the method of presentation, allowing a deeper level of understanding from the stakeholders. The framework as presented is most applicable to large restoration projects due to the requirement of having several reference sites as well as several set-aside sites to establish the boundaries of the restoration space. With few set-asides and reference sites, seasonal and measurement variability may result in instability in the index values, lessening the usefulness of the index. In practice, it is recommended that there be at least four reference and set-aside sites. While projects of this size are currently rare, the increasing importance of restoration and replacement of ecosystems suggests that this framework may have increased opportunities for use in the future. 5. Conclusions Measurements that are poorly correlated with the identified environmental stressors, exhibit high degrees of measurement variability, or are similarly represented by other metrics are eliminated during the filtering process, reducing future monitoring time and effort.The filtering process identified visual oiling estimates, density measurements of Pirinella, Cerithidea, Suaeda, and Salicornia as candidates for elimination.The most descriptive measures to date in terms of responsiveness to the stressors are Amphipod density, Nasima density, and estimates of species richness.Undesirable developments during the rehabilitation, such as the development of the carbonate crusts, may be considered as deviations from the preferred trajectory and should trigger an adaptive management responseMMIs are particularly useful for their ability to integrate multiple parameters into a cohesive, useful index. Established concepts in restoration including trajectory, alternate states, and restoration targets can be represented with minimal effort for entire restoration sites or for specific attributes of restored ecosystems. Acknowledgements This study was funded by United Nations Compensation Commission awards 5000451 ‘‘Remediation of damage to coastal
834
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
resources’’ and 5000456 ‘‘Remediation of damage to marine resources’’, and sponsored by the Presidency for Meteorology and Environment of the Kingdom of Saudi Arabia. We would like to thank S. Allen, M. Bice, J. Gabriel, A. Kopinski, and T. Minter for their assistance in the field program. We also would like to thank J. Michel and T. Montello for their editing assistance, and D. Little and M. Guard for their comments and suggestions during the development of this project. References Abuzinada, A.H., Krupp, F., 1994. The status of coastal and marine habitats two years after the Gulf War oil spill. Courier Forschungsinstitut Senckenberg 166, 1–80. Adam, P., 1990. Salt Marsh Ecology. Cambridge University Press, Cambridge, UK. Allen, E.A., Fell, P.E., Peck, M.A., Gieg, J.A., Guthke, C.R., Newkirk, M.D., 1994. Gut contents of common mummichogs, Fundulus heterclitus, in a restored impounded marsh and in natural reference marshes. Estuaries 17, 462–471. Al-Thukair, A.A., Al-Hinai, K., 1993. Preliminary damage assessment of algal mat sites located in the western gulf following the 1991 oil spill. Mar. Pollut. Bull. 27, 229–238. Al-Zaidan, A.S.Y., Kennedy, H., Jones, D.A., Al-Mohanna, S.Y., 2006. Role of microbial mats in Sulaibikhat Bay (Kuwait) mudflat food webs: evidence from 13C analysis. Mar. Ecol. Prog. Ser. 308, 27–36. Barth, H., 2003. The influence of cyanobacteria on oil polluted intertidal soils at the Saudi Arabian Gulf shores. Mar. Pollut. Bull. 46, 1245–1252. Bejarano, A.C., Michel, J., 2010. Large-scale risk assessment of polycyclic aromatic hydrocarbons in shoreline sediments from Saudi Arabia: environmental legacy after twelve years of the Gulf war oil spill. Environ. Pollut. 158, 1561–1569. Blocksom, K.A., 2003. A performance comparison of metric scoring methods for a multimetric index for mid-Atlantic highland streams. Environ. Manage. 31, 670–682. Bradshaw, A.D., 1987. The reclamation of derelict land and the ecology of ecosystems. In: Jordan, W.R., Gilpin, M.E., Aber, J.D. (Eds.), Restoration Ecology: A Synthetic Approach to Ecological Research. Cambridge University Press, Cambridge, UK, pp. 53–74. Brawley, A.H., Warren, R.S., Askins, R.A., 1998. Bird use of restoration and reference marshes within the Barn Island Wildlife Management Area, Stonington, Connecticut. USA. Environ. Manage. 22, 625–633. Broome, S.W., Seneca, E.D., Woodhouse Jr., W.W., 1988. Tidal salt marsh restoration. Aquat. Bot. 32, 1–22. Callaway, R.M., King, L., 1996. Temperature-driven variation in substrate oxygenation and the balance of competition and facilitation. Ecology 77, 1189–1195. Chamberlain, R.H., Barnhart, R.A., 1993. Early use by fish of a mitigation salt marsh, Humboldt Bay, California. Estuaries 16, 769–783. D’Alpaos, A., Lanzoni, S., Marani, M., 2005. Tidal network ontogeny: channel initiation and early development. J. Geophys. Res. 110, 1–14. Davis, W.S., Simon, T.P. (Eds.), 1989. Proceedings of the 1989 Midwest Pollution Control Biologists Meeting. United States Environmental Protection Agency, Chicago, Illinois, USA. EPA-905/9-89-007. Davis, N.M., Weaver, V., Parks, K., Lydy, M.J., 2003. An assessment of water quality, physical habitat, and biological integrity of an urban stream in Wichita, Kansas, prior to restoration improvements (phase I). Arch. Environ. Con. Tox. 44, 351– 359. Dobson, A.P., Bradshaw, A.D., Baker, A.J., 1997. Hopes for the future: restoration ecology and conservation biology. Science 277, 515–522. Downs, C.A., Dillon Jr., R.T., Fauth, J.E., Woodley, C.M., 2001a. A molecular biomarker system for assessing the health of gastropods (Ilyanassa obsoleta) exposed to natural and anthropogenic stressors. J. Exp. Mar. Biol. Ecol. 259, 189–214. Downs, C.A., Fauth, J.E., Woodley, C.M., 2001b. Assessing the health of grass shrimp (Paleaomonetes pugio) exposed to natural and anthropogenic stressors: a molecular biomarker system. Mar. Biotechnol. 3, 380–397. Duarte, C.M., 1991. Variance and the description of nature. In: Cole, J.J., Lovett, G.M., Findlay, S.E.G. (Eds.), Comparative Analyses of Ecosystems. Springer–Verlag, New York, USA, pp. 301–318. Ehrenfeld, J.G., 2000. Defining the limits of restoration: the need for realistic goals. Restor. Ecol. 8, 2–9. Emery, E.B., Simon, T.P., McCormick, F.H., Angermeier, P.L., Deshon, J.E., Yoder, C.O., Sanders, R.E., Pearson, W.D., Hickman, G.D., Reash, R.J., Thomas, J.A., 2003. Development of a multimetric index for assessing the biological condition of the Ohio River. T. Am. Fish. Soc. 132, 791–808. EPA, 1987. Surface water monitoring: a framework for change. Office of Water, Office of Policy Planning and Evaluation, United States Environmental Protection Agency, Washington, D.C., USA. Getter, C.D., Michel, J., Hayes, M.O., 2005. Natural recovery of salt marshes from the 1991 oil spill on the Saudi Arabian gulf coast, in: Proceedings of the 2005 International Oil Spill Conference, May 2005. Miami, Florida, USA. Gómez Gesteira, J.L., Dauvin, J.C., 2000. Amphipods are good bioindicators of the impact of oil spills on soft-bottom macrobenthic communities. Mar. Pollut. Bull. 40, 1017–1027. Hale, J.H., Cormack, C.D., Cotsapas, L., Montello, T.M., Langman, O.C., Gabriel, J.J., Michel, J., 2011. Relationships between key indicators of environmental
condition and degrees of oiling in sediments in salt marsh habitats: a balance between contamination and ecological recovery by natural processes, in: Proceedings of the 2011 International Oil Spill Conference, May 23-26 2011, Portland, Oregon, USA. Harris, P.M., Kendall, C.G.St.C., Lerche, I., 1985. Carbonate cementation —a brief review. In: Schneidermann, N., Harris, P.M. (Eds.), Carbonate Cements, SEPM Special Publication, vol. 36. Tulsa, Oklahoma, USA, pp. 79–95. Hobbs, R.J., Mooney, H.A., 1993. Restoration ecology and invasions. In: Saunders, D.A., Hobbs, R.J., Erlich, P.R. (Eds.), Nature conservation 3: reconstruction of fragmented ecosystems, global and regional perspectives. Surrey Beatty and Sons, Chipping Norton, New South Wales, Australia, pp. 127–133. Hobbs, R.J., Norton, D.A., 1996. Towards a conceptual framework for restoration ecology. Restor. Ecol. 4, 93–110. Höpner, T., Al-Shaikh, K.A., 2008. Shoreline bioremediation after the 1991 Gulf war oil spill. In: Abuzinada, A.H., Barth, J., Krupp, F., Böer, B., Abdessalaam, T.Z. (Eds.), Protecting the Gulf’s Marine ecosystems from Pollution. Basel, Switzerland, pp. 265–272. Horne, J.K., Schneider, D.C., 1995. Spatial variance in ecology. Oikos 74, 18–26. Howard, R.J., Mendelssohn, I.A., 1999. Salinity as a constraint on growth of oligohaline marsh macrophytes. Am. J. Bot. 86, 785–794. Hughes, F.M., Colston A., Mountford, J.O., 2005. Restoring riparian ecosystems: The challenge of accommodation variability and designing restoration trajectories. Ecol. Soc. 10:12. (online) url:
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835 Palmer, M.A., Poff, N.L., 1997. The influence of environmental heterogeneity on patterns and processes in streams. J. N. Am. Benthol. Soc. 16, 169–173. Pandion Technology, Ltd., Research Planning, Inc. 2003. Oiled shoreline survey in support of the marine and coastal damage assessment. Presidency of Meteorology and the Environment, Jeddah, Kingdom of Saudi Arabia. Pearson, D.L., Cassola, F., 1992. World-wide species richness patterns of Tiger Beetles (Coleoptera: Cicindelidae): Indicator taxon for biodiversity and conservation studies. Conserv. Biol. 6, 376–391. Peck, M.A., Fell, P.E., Allen, E.A., Gieg, J.A., Cuthke, C.R., Newkirk, M.D., 1994. Evaluation of tidal marsh restoration: comparison of selected macroinvertibrate populations on a restored impounded valley marsh and an unimpounded valley marsh within the same salt marsh system in connecticut. USA. Environ. Manage. 18, 283–293. Peterson, B.J., Howarth, R.W., Garritt, R.H., 1986. Sulfur and carbon isotopes as tracers of salt-marsh organic matter flow. Ecology 67, 865–874. Phillips, J.D., 1999. Divergence, convergence, and self-organization in landscapes. Ann. Assoc. Am. Geogr. 89, 466–488. Portnoy, J.W., 1999. Salt marsh diking and restoration: biogeochemical implications of altered wetland hydrology. Environ. Manage. 24, 111–120. Prober, S.M., Thiele, K.R., Lunt, I.D., 2002. Identifying ecological barriers to restoration in temperate grassy woodlands: soil changes associated with different degradation states. Aust. J. Bot. 50, 699–712. Reeves, G.H., Hall, J.D., Roelofs, T.D., Hickman, T.L., Baker, C.O., 1991. Rehabilitating and modifying stream habitats. In: Meehan, W.R. (Ed.), Influences of forest and rangeland management on salmonid fishes and their habitats, American Fisheries Society Special Publication 19. Bethesda, Maryland, USA, pp. 519–557. Reynoldson, T.B., 1987. Interactions between sediment contaminants and benthic organisms. Hydrobiologia 149, 53–66. Roni, P., Liermann, M., Steel, A., 2003. Monitoring and evaluating responses of salmonids and other fishes to instream restoration. In: Montgomery, D.R., Bolton, S., Booth, D.B. (Eds.), Restoration of Puget Sound rivers. University of Washington Press, Seattle, Washington, USA, pp. 318–329. Sabean, J.A.R., Scott, D.B., Lee, K., Venosa, A.D., 2009. Monitoring oil spill bioremediation using marsh foraminifera as indicators. Mar. Pollut. Bull. 59, 352–361.
835
Sacco, J.N., Seneca, E.D., Wentworth, T., 1994. Infaunal community development of artificially established salt marshes in North Carolina. Estuaries 17, 489– 500. Shreffler, D.K., Thom, R.M., Scott, M.J., Wellman, K.F., Walters, M.A., Curran, M., 1995. National Review of Non-Corps Environmental Restoration Projects. Pacific Northwest National Laboratory, Richland, Washington. Final Report Prepared for US Army Corps of Engineers, Institute for Water Resources, Alexandria, VA, IWR Report 95-R-12. Simenstad, C.A., Thom, R.M., 1996. Functional equivalency trajectories of the restored Gog-Le-Hi-Te estuarine wetland. Ecol. Appl. 6, 38–56. Society for Ecological Restoration, 2004.The SER international primer on ecological restoration, version 2. Retrieved December 4, 2010, from http://www.ser.org. Stoddard, J.L., Herlihy, A.T., Peck, D.V., Hughes, R.M., Whittier, T.R., Tarquinio, E., 2008. A process for creating multimetric indices for large-scale aquatic surveys. J. N. Am. Benthol. Soc. 27, 878–891. Tawfiq, N.I., Olsen, D.A., 1993. Saudi Arabia’s response to the 1991 gulf oil spill. Mar. Pollut. Bull. 27, 333–345. Tilman, D., 1996. Biodiversity: population versus ecosystem stability. Ecology 77, 350–363. Walker, B., Kinzig, A., Langridge, J., 1999. Plant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor species. Ecosystems 2, 95–113. Warren, R.S., Fell, P.E., Rozsa, R., Brawley, A.H., Orsted, A.C., Olson, E.T., Swamy, V., Niering, W.A., 2002. Salt marsh restoration in connecticut: 20 years of science and management. Restor. Ecol. 10, 497–513. Weisburg, S.B., Lotrich, V.A., 1982. The importance of an infrequently flooded intertidal marsh surface as an energy source for mummichog Fundulus heteroclitus: an experimental approach. Mar. Biol. 66, 307–310. Yachi, S., Loreau, M., 1999. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. P. Natl. Acad. Sci. USA 96, 1463–1468. Zedler, J.B. (Ed.), 2001. Handbook for restoring tidal wetlands. CRC Press, Boca Raton, Florida, USA. Zedler, J.B., Callaway, J.C., 1999. Tracking wetland restoration: do mitigation sites follow desired trajectories? Restor. Ecol. 7, 69–73.
Contents lists available at SciVerse ScienceDirect
Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Developing multimetric indices for monitoring ecological restoration progress in salt marshes O.C. Langman a,⇑,1, J.A. Hale a,1, C.D. Cormack a,1, M.J. Risk b, S.P. Madon c,2 a
Pandion Technology Ltd., Suite #601, 6th Floor, 28th October Street, Limassol 4065, Cyprus PO Box 1195, Durham ON, Canada N0G 1R0 c CH2M HILL, Water Resources and Ecosystems Management, Ecosystem Planning and Restoration, 402 W. Broadway, Suite 1450, San Diego, CA 92101, USA b
a r t i c l e
i n f o
Keywords: Multimetric index Salt marsh Ecological restoration End states of restoration Restoration trajectory Adaptive management
a b s t r a c t Effective tools for monitoring the status of ecological restoration projects are critical for the management of restoration programs. Such tools must integrate disparate data comprised of multiple variables that describe restoration status, including the condition of environmental stressors, landscape connectivity, ecosystem resilience, and ecological structure and function, while communicating these concepts effectively to a wide range of stakeholders. In this paper we describe the process of constructing multimetric indices (MMIs) for monitoring restoration status for restoration projects currently underway on the eastern coast of Saudi Arabia. During this process, an initial suite of measurements is filtered for response and sensitivity to ecosystem stressors, eliminating measurements that provide little information and reducing future monitoring efforts. The retained measurements are rescaled into comparable domain metrics and assembled into MMIs. The MMIs are presented in terms of established restoration theories, including restoration trajectory and restoration endpoint targets. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Degradation of aquatic ecosystems, primarily from anthropogenic activities, has led to major efforts to regenerate, rehabilitate, or convert ecosystems towards a more desirable configuration (National Research Council, 1992). The motivation behind restoration projects is the restoration of ecological services and functions, which impact a wide range of stakeholders beyond restoration scientists that will need to be informed of the progress of the project. Despite the large body of theory that supports the development and design of restoration projects, it has been pointed out (Jones and Schmitz, 2009; Reeves et al., 1991;Roni et al., 2003) that monitoring efforts have often proven inadequate to quantify physical and biological responses within the ecosystems being modified. Given this possibility of failure and the importance of communicating ecological information to stakeholders, monitoring programs need to play several roles, including: (1) integrating the scientific knowledge and theories behind the design of the project into the monitoring program to include the measurement of appropriate stressor and response variables, (2) developing and implementing an analytical framework that evaluates monitoring data to provide the pertinent information needed to adaptively manage the ⇑ Corresponding author. Tel.: +1 608 3207761; fax: +1 803 2546445. 1 2
E-mail address: [email protected] (O.C. Langman). Tel.: +1 803 5135649; fax: +1 803 2546445. Tel.: +1 619 6870120x37233.
0025-326X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpolbul.2012.01.030
restoration to improve its chances of success, and (3) presenting the progress and condition of the restored ecosystem to the stake holders in a manner that is easily interpretable and understandable, yet based on valid scientific assessments. 1.1. Endpoints of restoration The endpoints of restoration have been described in terms of community structure as well as supporting chemical, biological, and physical processes (National Research Council, 1992). Descriptions matching this level of detail for the desired state of remediation sites are rare, which has lead to the practice of having reference ecosystems provide the basis for both developing remediation methodology and evaluating the progress of an ecosystem restoration (Society for Ecological Restoration, 2004). While a reference system can be used as a model for a desirable outcome of restoration, the restored site will at best approximate the condition of the reference site due to spatial variability, however, slight, in the physical, chemical, and biological gradients forming the basis of the ecosystem processes. Further variability within a reference system emerges from the innate non-static nature of an ecosystem, across season variability, community-level evolution, or natural progression of the reference systems to new states (Duarte, 1991; Horne and Schneider, 1995; Palmer and Poff, 1997). Sadly, the desire to force an ecosystem into an overly specified state is common, and has resulted in restoration ‘failures’ that are, for the most part, functional ecosystems in their own right (Simenstad and Thom, 1996).
821
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
More stress
Ecosystem stressors
Less stress
Remains the same
Degraded ecosystem Time
Target ecosystem
Re ha bi lita De tio gr Na n (p a t da rim ur a t io ar l p n y r su oce cc ss es e sio s n)
Alternate states
Ecosystem Function
Ecosystem Complexity / Function Stressors reduced via remediation activities
Degraded ecosystem
C
Target ecosystem
Rec (rep lamatio lace n men t)
B
Target ecosystems
Ecosystem health responds to reductions in stressors
Ecosystem health
A
Degraded ecosystem Ecosystem structure
Fig. 1. Theoretical spaces that describe the response of an ecosystem to remediation. (A) An original space that assesses the response of ecosystem health to the reduction of stressors (B) A mapping of ecosystem complexity through time, redrawn from Hobbs and Mooney (1993) (C) Evaluating ecosystem function (biomass, nutrient content, cycling) against structure (species diversity, complexity), redrawn from Dobson et al. (1997).
1.2. Trajectory Equally as important as the restoration endpoint is the progression from degraded to a restored ecosystem. Numerous conceptual models for the progression of a restoration site through time have been proposed (Dobson et al., 1997; Hobbs and Mooney, 1993; Hughes et al., 2005; Magnuson et al., 1980), with the term ‘‘trajectory’’ being used to describe the hypothetical pathway traversed during the restoration progress (Fig. 1).The multiple interpretations of restoration trajectory are based on which ecosystem attributes (e.g., ecosystem health, structure, and function) are being tracked (Fig. 1) indicating the difficulty of consolidating the requirements of ecological restoration even at a conceptual level. As a reflection of this, the theoretical spaces leave trajectories simplified, indicating a general direction and approximate endpoint. Practical applications of the trajectory concept have largely involved developing multiple trajectories for individual parameters, often indicator species, used to represent restoration status. The inherent variability of single parameters, for example, over stressor gradients and temporal/spatial scales, however, often results in inconclusive representation of restoration trajectory (Odum et al., 1995;Zedler and Callaway, 1999), and aggregating parameters into a single trajectory has proven difficult (Society for Ecological Restoration, 2004). An ideal trajectory would integrate disparate data that describe site condition (and thus restoration status), and provide information that may be used to adaptively manage the restoration project. 1.3. Adaptive management Without a regular assessment of restoration status supported by a well-developed monitoring program, a restoration site may follow a trajectory different from the desired outcome. While our understanding of succession is continually improving, knowledge of the current state of an ecosystem and the stresses that it will face during restoration will never be complete, leading to difficulty in making accurate predictions of site evolution over the duration of the restoration project. The proposed solution to this problem is to monitor the restoration status and to nudge the system toward the desired trajectory and adaptively manage it if a significant deviation is detected. According to Shreffler et al. (1995) , this concept is not new, but there are few examples in the literature that indicate the principle is being used. Possible reasons for this include insufficient funding for additional manipulation, lack of clear resolutions to the problems, or a lack of supporting data to drive
the management. In the latter case, the decision to re-engage in the manipulation of the restoration site can be improved by providing managers with a broader dataset that describes ecosystem status, and by extension, the range of problems that can occur during restoration. Actually describing the concepts of trajectory, restoration endpoints, and adaptive management within the context of a monitoring program remains a difficult task. Recent attempts at describing ecosystem status have moved in two distinct directions: (1) identifying organisms that can be used to integrate multiple signals from the ecosystem (indicator species); or (2) by collecting large amounts of data to produce community descriptors. Indicator species have been developed as the corner stone of monitoring programs (Metcalfe et al., 1984;Reynoldson, 1987). Monitoring programs based on indicator species are particularly attractive because acquiring the data is often time- and cost-effective. Several common assumptions about the relationship of indicator species with the greater community have proven unreliable, however, such as the idea that high species richness or habitat diversity is correlated with the occurrence of rare species (Pearson and Cassola, 1992), or that associations between species remain similar across a given habitat (Niemi et al., 1997). When evaluating ecosystem status, particularly in restoration projects where the successional trajectory can be short-cut through plantings, species introductions, and other modifications, the lack of reliability of these relationships reduces the value of using just species–community relationships for evaluating the condition of the ecosystem. Complex systems need to be described using a framework of many parameters, although this task can overwhelm the researcher with data. Important parameters for ecosystems include elements of structure, function, landscape connectivity, and resilience to perturbations, all of which must be addressed to evaluate the status of a restoration. Karr (1981) introduced a multimetric index (MMI) to represent elements of biological condition in a variety of different systems. Karr’s work stemmed from the use of water quality data as a surrogate for biotic assessment, in cases when biological condition could not adequately be characterized. Until then, water quality was primarily monitored chemically and physically by the EPA and other monitoring institutions, but despite extensive monitoring and management programs water quality continued to deteriorate (Davis and Simon, 1989; EPA, 1987; Karr, 1981). This resulted from not only having excess chemical data that were swamping managers, but from a lack of data on the biological processes working on the ecosystems (Karr, 1981). Since its introduction as a method of monitoring biotic condition
822
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
in streams, the use of MMIs has expanded to other ecosystems, but has proven particularly useful in aquatic ecosystems (Emery et al., 2003; Stoddard et al., 2008). The purpose of this paper is to use monitoring data to develop an MMI that integrates disparate datasets and produces results that can guide restoration efforts and provide useful information to managers. The MMI will be based on preliminary monitoring data from a salt marsh restoration project currently underway, located on the eastern coastline of Saudi Arabia, developed in response to persistent degradation that was a result of the largest oil spill in history. At the conclusion of the 1991 Gulf War, an estimated 6–11 million barrels of crude oil were intentionally released into the Arabian Gulf, affecting approximately 800 km of the Saudi Arabian coastline (Abuzinada and Krupp, 1994; Tawfiq and Olsen, 1993). Both primary and secondary (faunal burrows) porosity allowed oil to penetrate the substrate of salt marshes and nearly two decades later, this oil persists. The United Nations
Compensation Commission was formed to administer monetary claims against Iraq, and awarded $463 million USD toward the ecological restoration of degraded salt marsh along the Saudi Arabian coast, creating one of the largest ecological restoration projects in history. We were tasked with developing and implementing a monitoring plan and an analytical framework for the monitoring data to estimate the status of post-remediation recovery, a task which included managing the restoration projects as well as incorporating local and international stakeholders into the decisionmaking process. The procedure used to generate the MMI is as follows: Identify attributes of the restoration site that may be areas of concern. Create a list of potential measurements useful for evaluating these attributes Condense these measurements into a set of metrics.
Fig. 2. Location of reference marshes (C1, C2, C3, C4, C5), restoration sites (R1, R2, R3, R4, R5, R6, R7, R8, R9, R10), and the set-aside sites (S1, S2, S3, S4).
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
Test these metrics for utility, based on initial monitoring. Eliminate those metrics that show no difference between restoration and reference sites, or that have high measurement or temporal variability. Evaluate reference sites to establish appropriate endpoints for remediation. Examine the current state of the restoration project in order to provide managers with data that can be used to adjust the trajectory of the project. 2. Materials and methods 2.1. Study area All field work was (and continues to be) conducted within intertidal salt marsh systems on the eastern coast of Saudi Arabia, between Abu Ali and Batinah Islands in the south, and the Balbol embayment to the north (Fig. 2). This segment of shoreline represented the area with the largest amount of stranded oil from the 1991 Gulf War oil spill. In the period between January and May of 1991, an estimated 6–11 million barrels of Kuwaiti crude oil were pumped or released into the Arabian Gulf, creating the largest oil spill in history (Abuzinada and Krupp, 1994; Tawfiq and Olsen, 1993). Much of the oil was trapped in the south due to orientation of the islands and bays between Abu Ali Island and Tanajib. Within these bays and islands, large areas of sheltered mudflats and intertidal marsh systems were moderately to heavily oiled (Fig. 3) during the initial spill, with oil penetrating particularly deeply in marshes due to an abundance of burrowing organisms (Getter et al., 2005; Pandion Technology Ltd., and Research Planning Inc., 2003; Michel et al., 2005). A follow-up survey performed in 2002–2003 mapped the extent and toxicity of the remnant oil over
823
12 years after the initial oiling. Marsh areas that were described as moderately oiled in the 2002–2003 survey report contain oil that, when flooded, produces sheen and small droplets of liquid oil. Areas that were described as heavily oiled produced thick patches of liquid oil under similar conditions (Fig. 3). Salt marshes were determined to contain over 23% of the remnant oiled sediments, with total PAH (sum of 43 PAHs) of ranging from 3580 ng/g to 126,900 ng/g in areas with visible remnant oil, which included all of the restoration marshes in this study. Bejarano and Michel (2010) found that heavily oiled salt marshes had total PAHs at concentrations that were above sediment quality benchmarks and PAH distributions indicating slow weathering rates compared to other habitats. These marshes were fine-grained, low-energy systems; tidal range was 0.5 m, and inputs of fresh water were minimal. Productivity was assumed to be low due to limitations imposed by extremes of heat and salinity: during summer, air temperatures regularly exceeded 45 °C, and salinity in the channels reached 90ppt. Although no studies could be located that quantified primary production for the marsh intertidal zones in the Arabian Gulf, it is assumed that algae were responsible for a higher fraction of primary productivity relative to marsh systems in other parts of the world (Al-Zaidan et al., 2006). The dominant vascular plants were two succulent perennials, Arthrocnemum macrostachyum and Halocnemum strobilaceum, and two succulent annuals, Salicornia europea and Suaeda maritima. Benthic algal mats comprised of diatoms and cyanobacteria often covered much of the marsh surface, with diverse morphologies influenced by elevation, hydrology, and nutrient gradients (Kendall et al., 1968). The crustacean community’s main constituent was Nasima dotilliforme, with Macrophthalmus sp. occupying some channel bottoms and low elevations extending into seaward mudflats, and the predatory
Fig. 3. Examples of various levels of oiling. (A) Heavy oiling located on the marsh surface due to oil trapped in non-burrow pore spaces. (B) Moderate oiling in channel bank due to oil trapped in relic burrows by laminate mat covering. (C) Moderate to light oil sheen in channel bank. (D) Moderate oiling from relic small (insect/polychaete) burrows in channel bank.
824
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
Fig. 4. Photograph of an excavation that shows a cross-section of laminated algal mat. Individual layers of sedimentation and algal growth are readily visible. Laminate mats grow as thick as 10 cm in some areas. An oil-lined burrow that was located below the laminate mat has been placed in the upper left of the photograph.
Metopograpsis messor, Metaplax indica, and Eurycarcinus orientalis appearing sporadically. Other invertebrates included the snails Pirinella conica and Nodilittorina sp., as well as numerous burrowing polychaetes and amphipods. Secondary effects from the oil spill have further impaired natural recovery of the salt marsh. In many areas of degraded salt marsh, the lack of burrowing fauna and algal grazers has allowed the algal mat to develop into a thick, laminated covering over the marsh surface (Fig. 4), impairing recovery by acting as a barrier to re-colonization, and preventing further weathering of the remaining oil (Al-Thukair and Al-Hinai, 1993; Barth, 2003, Pandion Technology Ltd., and Research Planning Inc., 2003). The thickness of the laminated mats ranged between 5 and 25 cm, with the thickest mat located in depressions on the surface of the marsh and degraded tidal channels. In these cases, the secondary effects were complicated further by the algal mat impairing marsh hydrology, which created a positive feedback with the algal mat, which thrived in areas with limited drainage. The remediation and restoration activities planned for the restoration sites include excavation of existing or impaired tidal channels (blocked primarily by algal mat), creation of new tidal channels, sediment tilling and direct treatment of algal mat, and transplantation of local flora, primarily Avicennia marina. While remnant oil still exists within these marshes, the focus of the restoration efforts will be on accelerating the natural recovery of these systems rather than the removal of oil. Most of the remnant oil in the marshes is contained within collapsed burrows of Nasima and other burrowing organisms, with oil deposits occurring at depths of up to 0.6 m (Pandion Technology Ltd., and Research Planning Inc., 2003; Michel et al., 2005). No feasible method for locating and extracting the oil from these burrows exists, and attempting to expose burrows through tilling or other invasive methods would impair any natural restoration that has occurred and potentially expose adjacent areas to re-oiling. Since some of the areas have recovered naturally to a large degree, the restoration activities will only be employed in areas where they can reasonably be expected to improve the sites. Many of the marshes targeted for restoration within this study appear to have stabilized in various states of natural recovery, but all are expected to benefit further from targeted restoration activities. 2.2. Site selection and sampling strategy Following an initial coastline survey in late 2009, marshes were prioritized for remediation activities based on a rapid assessment protocol that focused on evaluation of overall ecosystem health
(Hale et al., 2011). Salt marshes were placed along a gradient of disturbance that ranged from no impact to heavy impact, based primarily on an ecological evaluation. Sites identified as having received little to no impact during the initial oiling event, or sites that have since been described as sufficiently recovered, were used as reference marshes. Sites that were initially heavily oiled and have since had little natural recovery were divided into sites targeted for remediation, and set-aside sites, which will not be restored and serve as a baseline for monitoring natural recovery. This study incorporated data from five reference sites. Four of the reference sites were not impacted by the initial spill, the fifth was deemed to have recovered sufficiently due to statistical similarity with the non-impacted sites, and has been classified as recovered by other authors (Höpner and Al-Shaikh, 2008). The initial set of five restoration sites was selected from a range of impact levels and levels of ecological recovery between a medium level of impact to a high level of impact. Reference sites were located close to impacted sites when possible, although the number of reference sites was limited due to the high extent of oiling that occurred along the coastline. Field sampling was divided into biannual efforts, with the first year’s monitoring efforts represented here. Fall sampling occurred between September and November of 2010, and spring sampling occurred between February and April of 2011. Spring sampling occurred over 19 sites, including 10 impacted sites, 5 reference sites, and 4 set-aside sites. The sampling windows helped to reduce the effect of temporal variability of the field measurements, which is occasionally a concern for MMIs (Blocksom, 2003). The field program was designed to detect changes in environmental attributes that differ between reference and restoration sites due to primary or secondary effects from the oiling, and to detect changes that were the direct result of, or affected by, the remediation activities performed during the salt marsh restoration. Salt marsh restoration sites ranged from around 5 ha to larger than 80 ha in size, making sub-sampling desirable for most aspects of monitoring, and particularly for determining species diversity and density. Within each site, natural recovery was most evident along channels and in the lower elevations of the marsh, possibly reflecting differing rates of recovery relative to the degree of tidal flushing and initial vertical oil penetration. To ascertain the relative importance of elevation and channel proximity to the rate of recovery, nine sub-sampling plots were located within each reference and restoration site. The nine plots were placed at three relative elevations on the marsh surface, at low, medium, and high elevations, which, based on the reference marshes, corresponded to three distinct communities that differ primarily in the abundance and spatial distribution of the organisms present. For example, Salicornia europea is found abundantly on the marsh surface in the low elevation, only along channels in the medium elevation, and is largely absent in the high elevation plots. Despite the marked differences among the communities, the actual elevation range for the marsh surface rarely exceeded 30 cm from the mudflats that marked the end of the low elevation to the toe of a narrow sand beach that typically marked the extent of the upper marsh. At each elevation, two plots were associated with channels, and one plot was placed in the interfluvial space on the marsh surface between channels. The channel-associated plots were placed adjacent to two different channels within the same channel network of different order. Depending on the configuration of the marsh, these channels may either to be first and second order, or second and third order, but the larger of the two channels was between 1 m and 3 m wide and the smaller was <1 m wide. Data were collected and archived independently for each plot within a site for each season, and later combined during analysis. It was hypothesized that elevation and proximity to the channel
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
825
Table 1 List of attributes of restored ecosystems (SER, 2004). Attribute
Description
1
The restored ecosystem contains a characteristic assemblage of the species that occur in the reference ecosystem and that provide appropriate community structure The restored ecosystem consists of indigenous species to the greatest practicable extent. All functional groups necessary for the continued development and/or stability of the restored ecosystem are represented or, if they are not, the missing groups have the potential to colonize by natural means. The physical environment of the restored ecosystem is capable of sustaining reproducing populations of the species necessary for its continued stability or development along the desired trajectory. The restored ecosystem apparently functions normally for its ecological stage of development, and signs of dysfunction are absent. The restored ecosystem is suitably integrated into a larger ecological matrix or landscape, with which it interacts through abiotic and biotic flows and exchanges. Potential threats to the health and integrity of the restored ecosystem from the surrounding landscape have been eliminated or reduced as much as possible. The restored ecosystem is sufficiently resilient to endure the normal periodic stress events in the local environment that serve to maintain the integrity of the ecosystem. The restored ecosystem is self-sustaining to the same degree as its reference ecosystem, and has the potential to persist indefinitely under existing environmental conditions.
2 3 4 5 6 7 8 9
networks may influence the rate of recovery, which required that the data be separable based on these criteria. Furthermore, seasonal data were kept separate to allow for analysis of marsh progression through time. The data collected at each plot included measurements that assessed elements of ecosystem structure, function, and remnant anthropogenic effects within each site. Using the SER’s nine attributes of restored ecosystems as a guide (Table 1), a wide range of measurements were taken to assess current ecosystem status (Society for Ecological Restoration, 2004). For each of the SER attributes that applied to this project, several measurements that will later provide the information for developing ecosystem metrics, and that would represent each attribute in the overall restoration status, were identified (Table 2). The initial field samplings included as many of these measurements as could feasibly be measured by the field crews, with the assumption that some of these measurements would prove to be non-informative for the overall monitoring program and would later be dropped from the field program. 2.3. Metric filtering Once the initial monitoring was complete, the measurements were further developed into metrics. A ‘metric’ was defined as a value that could be derived from one or more discrete or continuous measurements of the marsh or surrounding landscape. Metrics largely coincided with the planned measurements in Table 2, but often the individual measurements could be utilized by one or more metrics, which were further broken down into components where possible. A metric that was derived from three measurements but could potentially be represented by a combination of two measurements or any individual measurement was developed into every possible combination. For example, the health of benthic infauna could be described as simple presence/absence of species, density measurements of the species, or by measurements of a subset of species present. A metric was developed for each possibility resulting in several, often highly correlated, metrics derived from the same source data. This process, combined with filtering at a later stage, resembles a rarefaction analysis. Categorical measurements required conversion before they can be interpreted as metrics. An example of this was the algal form observations, which were converted into a rank value arranged from least to most desirable. Algal forms that were regularly observed in reference marshes are assigned a value of 1, while polygonal laminate and flat laminate algal forms were assigned values of 3 and 5, respectively, representing their relative impact on hydrology and impairment to colonization (Fig. 5).
2.3.1. Range Metrics that have limited ranges were not useful for describing differences between marsh systems. Discrete variables such as rare organism abundances may lack the statistical power needed to provide useful information. Continuous variables occasionally exhibit measurement error that exceeds the range of the measurement. To address these concerns, discrete metrics were eliminated if their range was 2 or less. Continuous measurements were eliminated if their range was within the estimate of error for the measurement. For example, visual estimates of percent cover were determined to be ±5% based on photographic post-analysis of field estimates, so if the range fell under the 10% threshold, the metric was discarded. The criteria for discarding percent cover metrics matches Klemm et al. (2003), although it was unclear how Klemm et al. arrived at their rejection threshold. Other continuous metrics had rejection thresholds ranging from 7% to 35%, and were created using estimates of measurement variability derived from repeated samplings using different people and equipment. Finally, any metric where >90% of the values were 0 was discarded, such as measurements of abundance of snails of the genus Cerithium, which were rarely found within either the reference or restoration sites. Metrics that failed the range test were eliminated from further consideration. 2.3.2. Response to disturbance Within the context of a restoration due to anthropogenic damage, a metric must have been be able to distinguish between reference and impacted sites. Each site was classified according to the initial survey as ‘‘impacted’’, ‘‘set-aside’’, or ‘‘reference.’’ Prior to remediation activities, the ‘‘set-aside’’ and ‘‘impacted’’ classifications were treated as a single classification. A one-way analysis of variance (ANOVA) was used to test each metric for its ability to classify set-aside/impacted and reference sites. Significant metrics (F-value significance of p 6 0.05) were preserved, while metrics that was incapable of distinguishing between set-aside/impacted and reference sites were discarded. 2.3.3. Signal-to-noise ratio Metrics that exhibited a large amount of within-site variability relative to the variability between sites did not contribute adequate information to the index. The signal-to-noise ratio was calculated as the variance over all of the sites (impacted, set-aside, and reference) as the signal, divided by the variance of the metric between seasonal visits at individual sites as the noise (Kaufmann et al., 1999). Various ratio thresholds for retaining metrics have been suggested during the development of other MMIs, ranging from >1.5 to >3 (Klemm et al., 2003; McCormick et al., 2001,
826
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
Table 2 Target measurements and SER attribute associations. SER attribute
*
Measurement
References
Characteristic assemblage of species, intact community structure Amphipod abundance Active ocypode burrow counts Benthic infauna abundance Species richness
Gómez Gesteira and Dauvin (2000) Adam (1990) Sacco et al. (1994) Jones and Richmond (1992), Jones et al. (1998)
Presence of invasive species* Invasive richness Invasive diversity
Mooney and Hobbs (2000) Mooney and Hobbs (2000)
Presence and condition of key functional groups Perennial halophyte abundance Perennial halophyte canopy cover Gastropod abundance Ocypode abundance
Adam (1990) Adam (1990) Peck et al. (1994) Adam (1990)
Physical environment which supports biota Channel, porewater, and ponded salinity Substrate temperature in rhizosphere Burrow abundance on channel banks Burrow abundance in perpendicular transect extending onto marsh surface
Broome et al. (1988), Portnoy (1999), Howard and Mendelssohn (1999) Callaway and King (1996), Lindig-Cisneros and Zedler (2002) Adam (1990) (Original)
Normal ecosystem function and development Marsh surface drainage/hydrology Subsurface hydrology Perennial growth rates
Montalto and Steenhuis (2004) Montalto and Steenhuis (2004), Osgood and Zieman (1998) Howard and Mendelssohn (1999)
Integration into landscape Fish abundance Fish size distributions Fish gut contents C,N,S stable isotope analysis Bird foraging Bird diversity
Chamberlain and Barnhart (1993) Madon et al. (2001), Weisburg and Lotrich (1982) Allen et al. (1994) Kwak and Zedler (1997), Peterson et al. (1986) Brawley et al. (1998) Warren et al. (2002)
No threats to adjacent systems Analyses of foraminifera
Morvan et al. (2004), Sabean et al. (2009)
Resistance and resilience Shannon–Weaver diversity Euclidean distance index for functional attribute diversity Molecular biomarkers
Tilman (1996), Yachi and Loreau (1999), Naeem and Li (1997) Walker et al. (1999) Downs et al. (2001a,b)
Self-organizing, self-sustaining ecosystem Channel morphology Annual halophyte abundance
D’Alpaos et al. (2005), Phillips (1999) Adam (1990)
No invasive species were detected in the study area.
respectively), but little support for the different thresholds was apparent. After examining the response of the metrics that demonstrated significant response to the stressor gradient with signal to noise ratios between 0 and 5, we determined that a threshold of 3 was a tolerable compromise between eliminating variables with limited information and ensuring that the SER attributes were adequately defined by the measurements collected. Fig. 6 demonstrates the difference between a metric with a high signal-to-noise ratio (Nasima density) and a low signal-to-noise ratio (percentage of exposed sediment within a quadrat), with the latter serving as an example of a rejection based on the signal-to-noise criteria. Further consideration is given to address issues of metric scoring below. 2.4. Metric scoring The diversity of values and types of measurements that comprised the metrics required that they be rescaled to comparable values before they were integrated into a single multimetric value. Specifically, sites were scored continuously to the range 0–100, with the lower threshold of 0 representing the worst or most undesirable condition and the upper threshold of 100 representing the most desired condition. Several metrics required transformations before they could be rescaled to address directionality and
distribution concerns (Table 3). Metrics that had higher values representing worse condition needed to be inverted to match the desired scheme. For example, algal mat cover, a percentage metric, was considered more degraded at higher degrees of cover, which was addressed by inverting the cover values for algal mat cover. Metrics that were recorded as percentages originally derived from count data needed to be arcsine transformed to convert from a binomial distribution to a normal distribution. All such transformations were applied prior to the rescaling. The low number of sites made performing a power analysis (as per Blocksom, 2003) impossible, but the guidance provided by Blocksom’s analyses aided in the choice of rescaling algorithms, resulting in the selection of a continuous scaling method. Several MMIs in the literature have used the 95th percentile value of a metric to determine the upper bound and the 5th percentile to determine the lower bound used in the transformation for a specific metric (Blocksom, 2003; Stoddard et al., 2008).The limited number of sites available in this restoration project, however, allowed individual sites to alter these bound values drastically, meaning that the use of percentile-derived boundaries for rescaling may provide limited benefit. Instead, the minimum and maximum observed values were used as the upper and lower bounds. In Eq. (1), S is the rescaled metric. The mean of the set of measurements M is linearly rescaled between 0 and 1 and then multiplied by 100.
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
827
Fig. 5. Photographs of three algal forms. (A) A typical ‘‘laminate’’ algal form, with an average thickness 52 mm. (B) A typical ‘‘folded’’ algal form, with an average thickness of 1 mm. Note that this form is regularly seen in the reference marshes. (C) A typical ‘‘polygonal laminate’’ algal form, with an average thickness of 48 mm.
100
Metric Value
75
50
25
0 Nasima density (Reference)
Nasima density (Impacted Sites)
% Exposed Sediment (Reference)
% Exposed Sediment (Impacted Sites)
Fig. 6. Demonstration of signal-to-noise ratios. Both the Nasima density metric and the percentage of exposed sediment (i.e., not covered by algal mat or asphalt pavement) metric have significant values for responsiveness to the disturbance, but the percentage of exposed sediment metric fails the signal-to-noise ratio test. Signal-to-noise values for Nasima density and percent exposed sediment are 3.14 and 1.21, respectively.
S¼
M lower 100 upper lower
ð1Þ
The inability to correct for potential outliers without drastically increasing the number of set-aside and reference sites indicated that reducing measurement and seasonal variability was
particularly important. Increasing the signal-to-noise threshold for metric inclusion reduced the potential for a variable metric to cause rescaling to affect interpretative capacity. For this reason, the signal-to-noise threshold of 3 was preferred over lower literature values to stabilize the edge effects from variability.
828
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835 Table 3 List of metrics and filtering statistics. Metric
Range (pass/fail)
Responsiveness (p-value)
Signal-to-noise
Transform
Algal form rank Algal mat% cover Amphipod density Arthrocnemum% cover Arthrocnemum count Asphalt pavement% cover Avicennia% cover Avicennia count Cerithidea density Channel belt transect – Nasima burrows Channel belt transect – total burrows Exposed sediment% cover Halocnemum% cover Halocnemum count Macrophthalmus density Metopograpsis density Micro-channel% cover Nasima density Nodilittorina density Ocypode predator:prey ratio Ocypode richness Pirinella density Ponded water% cover Pneumatophore density Salicornia density Salinity – channel Salinity – ponded Water Salinity – porewater Scopimera density Sediment temp in rhizosphere Shannon index – all organisms Small insect burrow density Snail richness Suaeda density Total perennial% cover Total perennials count Total snail density Total species richness Visual oiling estimate Whole plot ponding
Pass Pass Pass Pass Pass Fail Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass
0.02 0.00 0.04 0.06 0.14 0.00 0.68 0.03 Undef 0.00 0.00 0.00 0.03 0.98 0.19 0.32 0.03 0.01 0.00 0.34 0.00 0.25 0.08 0.68 0.65 0.11 0.74 0.35 0.48 0.00 0.04 0.59 0.16 0.78 0.04 0.08 0.25 0.03 0.83 0.05
1.19 44.82 5.26 1.09 1.10 1.80 Undef 2.99 Undef 22.35 27.03 1.46 4.61 126.28 1.60 20.47 12.07 3.78 1.80 25.32 4.61 12.78 3.65 1647.59 3.66 4.30 7.73 1.19 0.89 37.51 3.17 1.29 2.59 3.29 126.78 1.41 13.10 4.35 3.73 3.59
None Invert None Arcsine None Invert Arcsin None None None None None Arcsin None None None None None None None None None Invert None None None None None None None None None None None Arcsine None None None None Invert
2.5. Categorization of datasets/treatment of redundancy Once the metrics were filtered and rescaled, the remaining survivors were grouped with the SER attribute that they most closely represent. After the metrics were assigned, there were several possibilities: (1) an SER attribute lacked any descriptive metrics; (2) an attribute was assigned exactly one metric; or (3) an attribute was represented by several metrics. In the first case, the metric either could not be evaluated for the site and needed to have additional metrics considered to evaluate the attribute or, more desirably, the attribute was not a concern for the remediation project. In the second case, the metric would directly represent that SER attribute. In the third case however, the metrics needed to be examined for redundancy and combined into a single metric. Redundancy was evaluated using Pearson’s product-moment correlation coefficient, with a rejection of a metric correlated with another at r P 0.75. If two metrics were redundant, the metric with a lower signal to noise ratio was discarded. The remaining metrics were added into a single index. The resulting combined values were rescaled. 3. Results This method of use and reuse of measurements produced an excess of metrics. This initial suite of measurements contained more information than strictly needed to evaluate marsh condition, with the assumption that many of the metrics would prove redundant or uninformative for evaluating restoration status. As a
consequence, the reduction of the set of metrics, and by extension the measurements taken during the field program, was a process integrated into the MMI development. Measurements that were rejected due to statistical insignificance or redundancy could be dropped from the sampling program altogether, which would allow researchers to focus valuable field time (and efforts) on relevant metrics. To aid this process, a single metric could be split into a gradient of complexity; if a metric was derived from two components and could potentially be represented by either one of those components, the individual components were included in case they were statistically sufficient to describe the desired attribute. For species presence and density metrics, often the metric was not significantly improved by incorporating the density of rare species, and so the effort needed to quantify the rare species can potentially be reduced. For example, the density of the predatory Metopograpsis was less informative (lower signal-to-noise ratio) due to high variability in densities than simply noting the presence or absence of the organism at a site. Metrics that had sufficient range, were repeatable (high signalto-noise ratio), and exhibited a response to the disturbance were integrated into the field program and monitored for two seasons, spring and fall. Whole ecosystem development and recovery were expected to be slow, with perennial vegetation and soil organic content potentially taking decades to recover to reference status. The most marked differences in salt marsh status between seasons will be visible during the spring recruitment period. Preliminary studies have shown rapid re-colonization of excavated channels by burrowing infauna, including amphipods and Nasima.
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
3.1. Metric selection Metrics were selected for possible inclusion into the MMIs out of a total of 72 possible metrics. Of the 72 metrics, nearly half (32) were alternative forms of metrics and were discarded as underperforming versions produced as part of the rarefaction analysis used to generate more efficient metrics. While this rarefaction approach produced an excess of metrics, the assumption was that most of these metrics were redundant, but occasionally a metric only required a subset of the sampling effort to obtain adequate information. In practice, only the best performing metric in terms of range, responsiveness to disturbance, and signal-to-noise ratio was preserved. Out of the remaining 40 metrics, 26 metrics were rejected (Table 3).Three of the metrics failed the basic range test, 19 failed the response test, and 4 failed the signal-to-noise ratio test. The visual oiling estimate failed to distinguish between reference and remediation sites. This was presumably a response to the methodology used to initially classify the sites; the initial classification considered the ecological condition, regardless of current visual oiling, allowing a recovered but oiled site to persist as a reference site. In addition, areas that contained relatively little remnant oil occasionally exhibited a large degree of secondary impact in the form of extensive algal mats. As a result, the visual presence of oil proved not to be a good metric. Instead, secondary effects that presumably developed in response to the oiling, including pervasive, laminated forms of algal mat and degraded hydrology, were used to represent the stressors acting on the marsh. 3.2. SER attribute axes Three of the nine SER attribute MMIs are presented here, each exhibiting a distinct difference in condition between the impacted and reference sites (Figs. 7–9). Attribute 4 (Fig. 9), which represents aspects of the physical environment that support local biota,
829
is of particular note. In this case, the impacted sites had a large range of variability. The differences were likely due to the densities of the tidal channel networks; algal mat has in-filled many of the channels at the sites that performed the most poorly on this axis (R5, R7, R8, R9), while some of the sites that performed markedly better had more intact networks (R2, R3). This difference reflects the remediation and restoration plans for these sites; the sites with better condition will receive less focus on physical environment. The other attributes showed similarly degraded conditions across the impacted sites, particularly across the attributes that represented floral and faunal structure (Figs. 7 and 8). From the pre-restoration plot of an individual attribute, it was possible to tell whether the attribute was degraded in the marsh system. An ANOVA with a significant f-value (see Figs. 7–9) indicated that the attribute in question was degraded relative to the reference marshes. If there was no detectible difference, the attribute was not considered degraded within the restoration sites. Comparison of magnitude across attribute MMIs was meaningless due to metric rescaling. An integrated MMI (Fig. 10) was created by taking the entire collection of metrics and incorporating all of them into a single MMI using the same process used for individual attribute MMIs. Some metrics included in individual attribute MMIs were excluded due to high correlation with metrics from other attributes, resulting in a smaller total subset of all of the considered metrics. Like the attribute MMIs, individual metrics were equally weighted. 3.3. Stressors axis The stressors MMI combined metrics that represented elements of marsh condition that were directly targeted by the restoration and remediation activities. In this case, the activities included expansion and repair of drainage networks, removal and breakup of the algal mats, and perennial transplants, which were designed to improve drainage, reduce laminate algal mat cover, and
Fig. 7. MMI describing the characteristic assemblage of species and community structure (SER attribute #1) plotted against a separate index describing the environmental stressors. The y-axis is comprised of the following metrics: species richness, amphipod presence and abundance, Nasima presence and abundance. The grey oval indicates the restoration target for this attribute. Significant difference between impacted and reference sites (ANOVA, p = 0.0010).
830
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
Fig. 8. MMI describing the presence and condition of the key functional groups of organisms (SER attribute #3) plotted against a separate index describing the environmental stressors. The y-axis is comprised of the following metrics: Shannon–Weaver (Shannon–Weiner) diversity index, perennial cover, and the presence and abundance of predatory ocypodes. The grey oval indicates the restoration target for this attribute. Significant difference between impacted and reference sites (ANOVA, p = 0.0002).
Fig. 9. MMI describing the condition of the physical environment (SER attribute #4) plotted against a separate index describing the environmental stressors. The y-axis is comprised of the following metrics: porewater salinity, relative sediment temperatures, and the abundance of evidence of burrowing crabs on channel banks. Significant difference between impacted and reference sites (ANOVA, p = 0.0076).
potentially increase the rate of degradation of the remaining oil. These activities were selected to offer the greatest chance of recovery for the effort expended, and do not necessarily represent attempts to address all of the stressors observed at each site. For
example, the removal of remnant oil (itself a stressor, particularly for burrowing fauna) from sealed infaunal burrows was determined to be infeasible due to the difficulty of locating burrows with pockets of remnant oil as well as actually extracting the oil. All of the re-
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
831
Fig. 10. MMI integrating all surviving metrics across all SER attributes. The y-axis is comprised of the following metrics: Nasima abundance, Amphipod abundance, perennial cover, integrated snail abundance, porewater salinity, relative sediment temperatures, and the abundance of evidence of burrowing crabs on channel banks. Significant difference between impacted and reference sites (ANOVA, p = 0.0001).
100 90 80 70 60 50 40 30 20 10 0
Fig. 11. Time series plot of the Ecosystem health MMI (Fig. 10) for three reference marshes. Marsh C1 exhibits marked seasonal differences that do not appear in marshes C2 or C3.
sponse axes (plotted on the vertical axis) were plotted against the same suite of stressors (plotted on the horizontal axis), since the primary concern of the monitoring program is to evaluate the effectiveness of the restoration and remediation techniques (Figs. 7–10). 3.4. Marsh restoration target A marsh restoration target (Figs. 7–10) was developed by calculating the standard deviation on the reference marshes
for each axis and creating an oval centered on the reference marshes to serve as a target. While this was contrived, it was useful because it integrated all of the elements of the SER attributes that described a successfully restored ecosystem, provided the attributes were represented and were given equal weight. Similar restoration targets may be developed for individual attributes, allowing for characterization of partial restoration successes. The restoration target for all of the marshes was defined as the space occupied by the reference marshes with a
832
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
buffer of one standard deviation of the variability of the target marshes. 3.5. Natural gradients No adjustments were made to address association between the metrics that may be correlated with the natural conditions that determined the initial degree of oiling. The topography of individual marshes varied greatly; several marshes contained dense dendritic channel networks and shallow pools while others lacked pools or displayed a lower density of tidal channels. The marshes were generally located across several different sheltered bays with surprisingly distinct tidal regimes due to two competing amphidromic centers, and sediments that were primarily muddy but with a varying sand component. Despite this surficial topography, sediment variability, and a 100 km distance between the northernmost and southernmost marshes involved in this study, the floral and faunal communities were similar across the reference marshes. It appears that the most important factor in determining the degree of oiling at a site was the predominant (NW) wind direction and magnitude during the initial oiling event (Pandion Technology Ltd., and Research Planning Inc., 2003).Thus, direction of the marsh exposure was correlated with many of the metrics which resulted in reference sites within the impacted area generally having a southern exposure. 3.6. Seasonal changes Due to seasonal rescaling and re-evaluation of metrics, it was hypothesized that seasonal differences would not significantly affect the output of the MMI generation process. One of the reference sites, however, suggests that this expectation was unfounded (Fig. 11). Site C1 exhibited a significant amount of seasonal variation derived from a robust infaunal community (primarily amphipods, Grandiderella sp.) that was delayed with respect to the other reference sites in the spring but which had established itself by the fall season sampling period. As a result, the MMIs that consider the infaunal community for the spring sampling periods rate site C1 poorly. Since other reference sites lacked that particular dynamic or other unique local seasonal changes, the seasonal variation was not as pronounced for other sites.
ecosystem, problems can be localized, and an adaptive adjustment can be implemented. 4.1. Marsh restoration target Restoration targets have been defined in many ways, such as providing habitat for a target species, meeting some minimum of functional performance, or meeting a structural requirement (Ehrenfeld, 2000;Zedler, 2001). In each case, reference systems that meet the requirements define the target ecosystem and likely span a gradient of potential conditions that will support the target goals. For this restoration project, the reference marshes were selected to include a range of healthy marsh configurations, partially due to the limited selection of reference sites due to the high degree of oiling along the coastline, but also to avoid over-specifying the restoration endpoint. Variability with the resulting ecosystems is tolerable as long as it resembles other systems in the area (Sacco et al., 1994). The configuration of the reference marshes varies in some measurements, but is remarkably similar in others. For example, the Arthrocnemum density metric had a range of 81 (after rescaling) for the reference marshes alone, indicating an immense degree of variability within the reference marshes for that metric. Channel density as estimated by analysis of satellite imagery and porewater salinity, however, were quite similar across all of the reference marshes. Porewater salinity in healthy sites ranged from 60 to 95 ppt, with a mean value of 82. Salinities at impacted sites were even higher, regularly exceeding the detection limits of the equipment (100 ppt), likely due to poor drainage (flushing) and the retention of water in the substrate. Variability of the traits is reflected in the size of the individual attribute targets, with larger acceptable ranges appearing for highly variable marsh characteristics. Despite these differences, the integrated, whole ecosystem MMI scores were similar (Fig. 10), indicating that there were likely several configurations that had similar overall complexity and organism densities represented within the reference marshes. Smaller restoration projects often do not have the benefit of a monitoring program large enough to characterize variability across multiple reference marshes. This is one possible reason why some projects fail to meet the specific criteria set out as restoration targets, despite the marsh forming into a viable and desirable ecosystem (Simenstad and Thom, 1996). 4.2. Trajectory
4. Discussion The primary concern of a restoration project is to initiate or accelerate the recovery of all attributes of a degraded ecosystem toward a more desirable state, with respect to ecosystem health, function, sustainability, and ongoing impacts to adjacent landscape (Society for Ecological Restoration, 2004). In practice, this has proven exceedingly difficult, which can be inferred based on the number and frequency of failed restoration projects (Hobbs and Norton, 1996; National Research Council, 1992). Failures happen for any number of reasons, including unexpected spatial and temporal variability in the physical setting, misunderstood relationships and interactions between resident organisms, and changes in the human desires driving the restoration. The process presented herein is derived from the example of establishing a monitoring framework for the restoration program on the eastern coastline of the Kingdom of Saudi Arabia. This methodology will be transferable to different habitats and different regions and will identify when the restoration as a whole is underperforming, and will present the relevant information in terms of the prevailing theories behind ecosystem restoration, including trajectory and restoration targets. By further compartmentalizing the attributes of a restored
The restoration trajectory concept is particularly intuitive, which makes it useful for communicating restoration status, of particular use when incorporating stakeholders into the decision making process. In most of the two-dimensional restoration spaces described in the literature (Fig. 1), restoration trajectories were represented as a smooth line leading directly from the degraded state in the lower left corner to the desired system state in the upper right corner. The MMI attribute and stressors spaces were intentionally set up to mirror this configuration. The trajectory concept, however, was likely oversimplified. In most of these spaces, ecological restoration was portrayed as degradation in reverse (Bradshaw, 1987). The recovery may involve feedbacks that are not reversible, however, such as salinization or nutrient shifts that impair recovery (Prober et al., 2002; Davis et al., 2003). Alternatively, the restoration activities may not promote response across all aspects of an ecosystem, and may negatively impact the site during restoration activities. While these incongruities and sources of variability will make the trajectory less clean, the overall movement from a degraded to a restored condition is preserved. Non-technical stakeholders will benefit greatly from the intuitive representation of restoration progress, potentially encouraging ownership in the project. If the ‘‘ecosystem health’’ descriptor is too generic (Fig. 10) for the
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
intended audience, the individual SER attribute MMIs can be presented using the same trajectory and restoration target concepts (for example, Figs. 7–9), further expanding depth of understanding while preserving the method and simplicity of interpretation. Trajectory in the attribute-stressor spaces presented in Figs. 7– 10 will not follow a strictly linear pathway from lower-left to upper-right due to non-linear response. The stressor axis represents the measureable elements that will be directly affected by activities undertaken to accelerate restoration at the site, including primarily metrics of hydrology and algal mat cover. After restoration activities are performed, which include direct manipulation of algal mat cover as well as increases in channel density and improved hydrology, the stressor index is expected to improve immediately, although not to the levels of the reference systems. Further desiccation and natural reduction in algal mat is expected as a result of increased drainage, which will only occur over an as yet unknown period of time, presumably at least several months, following the restoration of hydrology and the re-introduction of grazers. The relative rates of recovery present many possibilities for recovery trajectory, all of which are desirable. A problem occurs if the attribute stabilizes outside of the acceptable restoration targets, or if it begins to decline toward a degraded state. For example, Harris et al. (1985) described a phenomenon in which carbonate crusts formed in the intertidal zone as a result of repeated wet/ dry cycles. Such crusts have been observed on tidal channel banks within the study region and appear to limit the habitat available for burrowing organisms and also provided hard substrate for the establishment of sessile species (particularly Euraphia sp.) that are foreign to a soft sediment environment. The development of cemented crusts post-restoration would drive the trajectory towards an undesirable endpoint. The rapidity of detecting changes in status of a restoration site depends on the rate of natural regeneration. In our case, evidence from preliminary trials suggested that infauna rapidly recolonize, particularly along channels, but there was little evidence for a rapid recovery of the perennial vegetation, possibly due to degraded hydrology or the establishment of laminated forms of algal mat. Similarly, attributes based largely on infaunal evidence are expected to change more quickly than attributes based on perennial vegetation. 4.3. Natural recovery As presented, these MMIs represent the components of ecosystem recovery that can be attributed to anthropogenic contributions. The set-aside sites are particularly important, since they will continue to move along their own, natural trajectory (not necessarily toward the restoration target) independent of the sites affected by remediation and restoration activities. The seasonal rescaling that is performed during the creating of the MMIs creates a moving bar against which progress is measured, however, as long as the set-asides and restoration sites were similar with respect to their conditions prior to the restoration activities, this simply results in the natural recovery (or decline) effectively being factored out. This makes evaluating the anthropogenic contribution to the marsh restoration apparent and assists in answering whether the restoration activities have aided site recovery, since movement within the MMI space should be a response to restoration activities. One potential issue with rescaling, however, is that the seasonal rescaling is particularly sensitive to measurement variability within the set-aside and reference sites. Measurement variability, particularly for the set-aside sites and reference sites, can result in shifting the MMI values several points in either direction, depending on the magnitude of the measurement variability. The mean value for the observed shift for the set-aside and reference sites in the Ecosystem Health MMI to date is 2.2, a measurement which can be used to track how much measurement
833
distortion is present in individual seasons. Increasing the number of reference sites and set-asides and reducing the measurement variability through any available means are both recommended to help deal with this issue. 4.4. SER attribute MMIs The development of individual MMIs for each restoration attribute is designed to provide additional information in the event that a restoration project is underperforming. Indeed, partial successes are the majority result for ecosystem restoration projects (Lockwood and Pimm, 1999). While it is not feasible to monitor every metric that could indicate underperformance in a restoration project, the SER attribute MMIs attempt to ensure that the subset of metrics that are monitored over the longterm at least track important and measurable differences between the impacted and reference systems. The stakeholders in a restoration project, usually composed of people of disparate backgrounds and education, ultimately determine when a restoration project will take place, the acceptable endpoints for the remediation, and when or if additional measures need to be taken to guide the natural development of a restoration site. The MMI framework developed herein presents restoration status in a way that closely matches elements of restoration theory that are particularly intuitive, including restoration targets and trajectories. The breakdown of the whole-ecosystem MMI into components along the lines of the SER attributes of a restored ecosystem further extends the interpretive capability by preserving the assumptions made from the method of presentation, allowing a deeper level of understanding from the stakeholders. The framework as presented is most applicable to large restoration projects due to the requirement of having several reference sites as well as several set-aside sites to establish the boundaries of the restoration space. With few set-asides and reference sites, seasonal and measurement variability may result in instability in the index values, lessening the usefulness of the index. In practice, it is recommended that there be at least four reference and set-aside sites. While projects of this size are currently rare, the increasing importance of restoration and replacement of ecosystems suggests that this framework may have increased opportunities for use in the future. 5. Conclusions Measurements that are poorly correlated with the identified environmental stressors, exhibit high degrees of measurement variability, or are similarly represented by other metrics are eliminated during the filtering process, reducing future monitoring time and effort.The filtering process identified visual oiling estimates, density measurements of Pirinella, Cerithidea, Suaeda, and Salicornia as candidates for elimination.The most descriptive measures to date in terms of responsiveness to the stressors are Amphipod density, Nasima density, and estimates of species richness.Undesirable developments during the rehabilitation, such as the development of the carbonate crusts, may be considered as deviations from the preferred trajectory and should trigger an adaptive management responseMMIs are particularly useful for their ability to integrate multiple parameters into a cohesive, useful index. Established concepts in restoration including trajectory, alternate states, and restoration targets can be represented with minimal effort for entire restoration sites or for specific attributes of restored ecosystems. Acknowledgements This study was funded by United Nations Compensation Commission awards 5000451 ‘‘Remediation of damage to coastal
834
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835
resources’’ and 5000456 ‘‘Remediation of damage to marine resources’’, and sponsored by the Presidency for Meteorology and Environment of the Kingdom of Saudi Arabia. We would like to thank S. Allen, M. Bice, J. Gabriel, A. Kopinski, and T. Minter for their assistance in the field program. We also would like to thank J. Michel and T. Montello for their editing assistance, and D. Little and M. Guard for their comments and suggestions during the development of this project. References Abuzinada, A.H., Krupp, F., 1994. The status of coastal and marine habitats two years after the Gulf War oil spill. Courier Forschungsinstitut Senckenberg 166, 1–80. Adam, P., 1990. Salt Marsh Ecology. Cambridge University Press, Cambridge, UK. Allen, E.A., Fell, P.E., Peck, M.A., Gieg, J.A., Guthke, C.R., Newkirk, M.D., 1994. Gut contents of common mummichogs, Fundulus heterclitus, in a restored impounded marsh and in natural reference marshes. Estuaries 17, 462–471. Al-Thukair, A.A., Al-Hinai, K., 1993. Preliminary damage assessment of algal mat sites located in the western gulf following the 1991 oil spill. Mar. Pollut. Bull. 27, 229–238. Al-Zaidan, A.S.Y., Kennedy, H., Jones, D.A., Al-Mohanna, S.Y., 2006. Role of microbial mats in Sulaibikhat Bay (Kuwait) mudflat food webs: evidence from 13C analysis. Mar. Ecol. Prog. Ser. 308, 27–36. Barth, H., 2003. The influence of cyanobacteria on oil polluted intertidal soils at the Saudi Arabian Gulf shores. Mar. Pollut. Bull. 46, 1245–1252. Bejarano, A.C., Michel, J., 2010. Large-scale risk assessment of polycyclic aromatic hydrocarbons in shoreline sediments from Saudi Arabia: environmental legacy after twelve years of the Gulf war oil spill. Environ. Pollut. 158, 1561–1569. Blocksom, K.A., 2003. A performance comparison of metric scoring methods for a multimetric index for mid-Atlantic highland streams. Environ. Manage. 31, 670–682. Bradshaw, A.D., 1987. The reclamation of derelict land and the ecology of ecosystems. In: Jordan, W.R., Gilpin, M.E., Aber, J.D. (Eds.), Restoration Ecology: A Synthetic Approach to Ecological Research. Cambridge University Press, Cambridge, UK, pp. 53–74. Brawley, A.H., Warren, R.S., Askins, R.A., 1998. Bird use of restoration and reference marshes within the Barn Island Wildlife Management Area, Stonington, Connecticut. USA. Environ. Manage. 22, 625–633. Broome, S.W., Seneca, E.D., Woodhouse Jr., W.W., 1988. Tidal salt marsh restoration. Aquat. Bot. 32, 1–22. Callaway, R.M., King, L., 1996. Temperature-driven variation in substrate oxygenation and the balance of competition and facilitation. Ecology 77, 1189–1195. Chamberlain, R.H., Barnhart, R.A., 1993. Early use by fish of a mitigation salt marsh, Humboldt Bay, California. Estuaries 16, 769–783. D’Alpaos, A., Lanzoni, S., Marani, M., 2005. Tidal network ontogeny: channel initiation and early development. J. Geophys. Res. 110, 1–14. Davis, W.S., Simon, T.P. (Eds.), 1989. Proceedings of the 1989 Midwest Pollution Control Biologists Meeting. United States Environmental Protection Agency, Chicago, Illinois, USA. EPA-905/9-89-007. Davis, N.M., Weaver, V., Parks, K., Lydy, M.J., 2003. An assessment of water quality, physical habitat, and biological integrity of an urban stream in Wichita, Kansas, prior to restoration improvements (phase I). Arch. Environ. Con. Tox. 44, 351– 359. Dobson, A.P., Bradshaw, A.D., Baker, A.J., 1997. Hopes for the future: restoration ecology and conservation biology. Science 277, 515–522. Downs, C.A., Dillon Jr., R.T., Fauth, J.E., Woodley, C.M., 2001a. A molecular biomarker system for assessing the health of gastropods (Ilyanassa obsoleta) exposed to natural and anthropogenic stressors. J. Exp. Mar. Biol. Ecol. 259, 189–214. Downs, C.A., Fauth, J.E., Woodley, C.M., 2001b. Assessing the health of grass shrimp (Paleaomonetes pugio) exposed to natural and anthropogenic stressors: a molecular biomarker system. Mar. Biotechnol. 3, 380–397. Duarte, C.M., 1991. Variance and the description of nature. In: Cole, J.J., Lovett, G.M., Findlay, S.E.G. (Eds.), Comparative Analyses of Ecosystems. Springer–Verlag, New York, USA, pp. 301–318. Ehrenfeld, J.G., 2000. Defining the limits of restoration: the need for realistic goals. Restor. Ecol. 8, 2–9. Emery, E.B., Simon, T.P., McCormick, F.H., Angermeier, P.L., Deshon, J.E., Yoder, C.O., Sanders, R.E., Pearson, W.D., Hickman, G.D., Reash, R.J., Thomas, J.A., 2003. Development of a multimetric index for assessing the biological condition of the Ohio River. T. Am. Fish. Soc. 132, 791–808. EPA, 1987. Surface water monitoring: a framework for change. Office of Water, Office of Policy Planning and Evaluation, United States Environmental Protection Agency, Washington, D.C., USA. Getter, C.D., Michel, J., Hayes, M.O., 2005. Natural recovery of salt marshes from the 1991 oil spill on the Saudi Arabian gulf coast, in: Proceedings of the 2005 International Oil Spill Conference, May 2005. Miami, Florida, USA. Gómez Gesteira, J.L., Dauvin, J.C., 2000. Amphipods are good bioindicators of the impact of oil spills on soft-bottom macrobenthic communities. Mar. Pollut. Bull. 40, 1017–1027. Hale, J.H., Cormack, C.D., Cotsapas, L., Montello, T.M., Langman, O.C., Gabriel, J.J., Michel, J., 2011. Relationships between key indicators of environmental
condition and degrees of oiling in sediments in salt marsh habitats: a balance between contamination and ecological recovery by natural processes, in: Proceedings of the 2011 International Oil Spill Conference, May 23-26 2011, Portland, Oregon, USA. Harris, P.M., Kendall, C.G.St.C., Lerche, I., 1985. Carbonate cementation —a brief review. In: Schneidermann, N., Harris, P.M. (Eds.), Carbonate Cements, SEPM Special Publication, vol. 36. Tulsa, Oklahoma, USA, pp. 79–95. Hobbs, R.J., Mooney, H.A., 1993. Restoration ecology and invasions. In: Saunders, D.A., Hobbs, R.J., Erlich, P.R. (Eds.), Nature conservation 3: reconstruction of fragmented ecosystems, global and regional perspectives. Surrey Beatty and Sons, Chipping Norton, New South Wales, Australia, pp. 127–133. Hobbs, R.J., Norton, D.A., 1996. Towards a conceptual framework for restoration ecology. Restor. Ecol. 4, 93–110. Höpner, T., Al-Shaikh, K.A., 2008. Shoreline bioremediation after the 1991 Gulf war oil spill. In: Abuzinada, A.H., Barth, J., Krupp, F., Böer, B., Abdessalaam, T.Z. (Eds.), Protecting the Gulf’s Marine ecosystems from Pollution. Basel, Switzerland, pp. 265–272. Horne, J.K., Schneider, D.C., 1995. Spatial variance in ecology. Oikos 74, 18–26. Howard, R.J., Mendelssohn, I.A., 1999. Salinity as a constraint on growth of oligohaline marsh macrophytes. Am. J. Bot. 86, 785–794. Hughes, F.M., Colston A., Mountford, J.O., 2005. Restoring riparian ecosystems: The challenge of accommodation variability and designing restoration trajectories. Ecol. Soc. 10:12. (online) url:
O.C. Langman et al. / Marine Pollution Bulletin 64 (2012) 820–835 Palmer, M.A., Poff, N.L., 1997. The influence of environmental heterogeneity on patterns and processes in streams. J. N. Am. Benthol. Soc. 16, 169–173. Pandion Technology, Ltd., Research Planning, Inc. 2003. Oiled shoreline survey in support of the marine and coastal damage assessment. Presidency of Meteorology and the Environment, Jeddah, Kingdom of Saudi Arabia. Pearson, D.L., Cassola, F., 1992. World-wide species richness patterns of Tiger Beetles (Coleoptera: Cicindelidae): Indicator taxon for biodiversity and conservation studies. Conserv. Biol. 6, 376–391. Peck, M.A., Fell, P.E., Allen, E.A., Gieg, J.A., Cuthke, C.R., Newkirk, M.D., 1994. Evaluation of tidal marsh restoration: comparison of selected macroinvertibrate populations on a restored impounded valley marsh and an unimpounded valley marsh within the same salt marsh system in connecticut. USA. Environ. Manage. 18, 283–293. Peterson, B.J., Howarth, R.W., Garritt, R.H., 1986. Sulfur and carbon isotopes as tracers of salt-marsh organic matter flow. Ecology 67, 865–874. Phillips, J.D., 1999. Divergence, convergence, and self-organization in landscapes. Ann. Assoc. Am. Geogr. 89, 466–488. Portnoy, J.W., 1999. Salt marsh diking and restoration: biogeochemical implications of altered wetland hydrology. Environ. Manage. 24, 111–120. Prober, S.M., Thiele, K.R., Lunt, I.D., 2002. Identifying ecological barriers to restoration in temperate grassy woodlands: soil changes associated with different degradation states. Aust. J. Bot. 50, 699–712. Reeves, G.H., Hall, J.D., Roelofs, T.D., Hickman, T.L., Baker, C.O., 1991. Rehabilitating and modifying stream habitats. In: Meehan, W.R. (Ed.), Influences of forest and rangeland management on salmonid fishes and their habitats, American Fisheries Society Special Publication 19. Bethesda, Maryland, USA, pp. 519–557. Reynoldson, T.B., 1987. Interactions between sediment contaminants and benthic organisms. Hydrobiologia 149, 53–66. Roni, P., Liermann, M., Steel, A., 2003. Monitoring and evaluating responses of salmonids and other fishes to instream restoration. In: Montgomery, D.R., Bolton, S., Booth, D.B. (Eds.), Restoration of Puget Sound rivers. University of Washington Press, Seattle, Washington, USA, pp. 318–329. Sabean, J.A.R., Scott, D.B., Lee, K., Venosa, A.D., 2009. Monitoring oil spill bioremediation using marsh foraminifera as indicators. Mar. Pollut. Bull. 59, 352–361.
835
Sacco, J.N., Seneca, E.D., Wentworth, T., 1994. Infaunal community development of artificially established salt marshes in North Carolina. Estuaries 17, 489– 500. Shreffler, D.K., Thom, R.M., Scott, M.J., Wellman, K.F., Walters, M.A., Curran, M., 1995. National Review of Non-Corps Environmental Restoration Projects. Pacific Northwest National Laboratory, Richland, Washington. Final Report Prepared for US Army Corps of Engineers, Institute for Water Resources, Alexandria, VA, IWR Report 95-R-12. Simenstad, C.A., Thom, R.M., 1996. Functional equivalency trajectories of the restored Gog-Le-Hi-Te estuarine wetland. Ecol. Appl. 6, 38–56. Society for Ecological Restoration, 2004.The SER international primer on ecological restoration, version 2. Retrieved December 4, 2010, from http://www.ser.org. Stoddard, J.L., Herlihy, A.T., Peck, D.V., Hughes, R.M., Whittier, T.R., Tarquinio, E., 2008. A process for creating multimetric indices for large-scale aquatic surveys. J. N. Am. Benthol. Soc. 27, 878–891. Tawfiq, N.I., Olsen, D.A., 1993. Saudi Arabia’s response to the 1991 gulf oil spill. Mar. Pollut. Bull. 27, 333–345. Tilman, D., 1996. Biodiversity: population versus ecosystem stability. Ecology 77, 350–363. Walker, B., Kinzig, A., Langridge, J., 1999. Plant attribute diversity, resilience, and ecosystem function: the nature and significance of dominant and minor species. Ecosystems 2, 95–113. Warren, R.S., Fell, P.E., Rozsa, R., Brawley, A.H., Orsted, A.C., Olson, E.T., Swamy, V., Niering, W.A., 2002. Salt marsh restoration in connecticut: 20 years of science and management. Restor. Ecol. 10, 497–513. Weisburg, S.B., Lotrich, V.A., 1982. The importance of an infrequently flooded intertidal marsh surface as an energy source for mummichog Fundulus heteroclitus: an experimental approach. Mar. Biol. 66, 307–310. Yachi, S., Loreau, M., 1999. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. P. Natl. Acad. Sci. USA 96, 1463–1468. Zedler, J.B. (Ed.), 2001. Handbook for restoring tidal wetlands. CRC Press, Boca Raton, Florida, USA. Zedler, J.B., Callaway, J.C., 1999. Tracking wetland restoration: do mitigation sites follow desired trajectories? Restor. Ecol. 7, 69–73.